Linoleoyl-lysophosphatidylcholine suppresses immune-related adverse events due to immune checkpoint blockade.

Immune related adverse events (irAEs) after immune checkpoint blockade (ICB) therapy occur in a significant proportion of cancer patients. To date, the circulating mediators of ICB-irAEs remain poorly understood. Using non-targeted mass spectrometry, here we identify the circulating bio-active lipid linoleoyl-lysophosphatidylcholine (LPC 18:2) as a modulator of ICB-irAEs. In three independent human studies of ICB treatment for solid tumor, loss of circulating LPC 18:2 preceded the development of severe irAEs across multiple organ systems. In both healthy humans and severe ICB-irAE patients, low LPC 18:2 was found to correlate with high blood neutrophilia. Reduced LPC 18:2 biosynthesis was confirmed in preclinical ICB-irAE models, and LPC 18:2 supplementation in vivo suppressed neutrophilia and tissue inflammation without impacting ICB anti-tumor response. Results indicate that circulating LPC 18:2 suppresses human ICB-irAEs, and LPC 18:2 supplementation may improve ICB outcomes by preventing severe inflammation while maintaining anti-tumor immunity.

Exploring the underlying molecular mechanisms of acute myocardial infarction after SARS-CoV-2 infection.

An increase in acute myocardial infarction (AMI)-related deaths has been reported during the COVID-19 pandemic. Despite evidence suggesting the association between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and AMI, the underlying mechanisms remain unclear. Here, we integrated mRNA and microRNA expression profiles related to SARS-CoV-2 infection and AMI from public databases. We then performed transcriptomic analysis using bioinformatics and systems biology approaches to explore the potential molecular mechanisms of SARS-CoV-2 infection affects AMI. First, twenty-one common differentially expressed genes (DEGs) were identified from SARS-CoV-2 infection and AMI patients in endothelial cells datasets and then we performed functional analysis to predict the roles of these DEGs. The functional analysis emphasized that the endothelial cell response to cytokine stimulus due to excessive inflammation was essential in these two diseases. Importantly, the tumor necrosis factor and interleukin-17 signaling pathways appeared to be integral factors in this mechanism. Interestingly, most of these common genes were also upregulated in transcriptomic datasets of SARS-CoV-2-infected cardiomyocytes, suggesting that these genes may be shared in cardiac- and vascular-related injuries. We subsequently built a protein-protein interaction network and extracted hub genes and essential modules from this network. At the transcriptional and post-transcriptional levels, regulatory networks with common DEGs were also constructed, and some key regulator signatures were further identified and validated. In summary, our research revealed that a highly activated inflammatory response in patients with COVID-19 might be a crucial factor for susceptibility to AMI and we identified some candidate genes and regulators that could be used as biomarkers or potential therapeutic targets.

Deep Learning-Derived Myocardial Strain.

BACKGROUND: Echocardiographic strain measurements require extensive operator experience and have significant intervendor variability. Creating an automated, open-source, vendor-agnostic method to retrospectively measure global longitudinal strain (GLS) from standard echocardiography B-mode images would greatly improve post hoc research applications and may streamline patient analyses. OBJECTIVES: This study was seeking to develop an automated deep learning strain (DLS) analysis pipeline and validate its performance across multiple applications and populations. METHODS: Interobserver/-vendor variation of traditional GLS, and simulated effects of variation in contour on speckle-tracking measurements were assessed. The DLS pipeline was designed to take semantic segmentation results from EchoNet-Dynamic and derive longitudinal strain by calculating change in the length of the left ventricular endocardial contour. DLS was evaluated for agreement with GLS on a large external dataset and applied across a range of conditions that result in cardiac hypertrophy. RESULTS: In patients scanned by 2 sonographers using 2 vendors, GLS had an intraclass correlation of 0.29 (95% CI: -0.01 to 0.53, P = 0.03) between vendor measurements and 0.63 (95% CI: 0.48-0.74, P < 0.001) between sonographers. With minor changes in initial input contour, step-wise pixel shifts resulted in a mean absolute error of 3.48% and proportional strain difference of 13.52% by a 6-pixel shift. In external validation, DLS maintained moderate agreement with 2-dimensional GLS (intraclass correlation coefficient [ICC]: 0.56, P = 0.002) with a bias of -3.31% (limits of agreement: -11.65% to 5.02%). The DLS method showed differences (P < 0.0001) between populations with cardiac hypertrophy and had moderate agreement in a patient population of advanced cardiac amyloidosis: ICC was 0.64 (95% CI: 0.53-0.72), P < 0.001, with a bias of 0.57%, limits of agreement of -4.87% to 6.01% vs 2-dimensional GLS. CONCLUSIONS: The open-source DLS provides lower variation than human measurements and similar quantitative results. The method is rapid, consistent, vendor-agnostic, publicly released, and applicable across a wide range of imaging qualities.

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.

Utilization of P2Y12 Inhibitors in Older Adults With ST-Elevation Myocardial Infarction and Frailty.

Choosing optimal P2Y12 inhibitor in frail older adults is challenging because they are at increased risk of both ischemic and bleeding events. We conducted a retrospective cohort study of Medicare Advantage Plan beneficiaries who were prescribed clopidogrel, prasugrel, or ticagrelor after percutaneous coronary intervention-treated ST-elevation myocardial infarction from January 1, 2010 to December 31, 2020. Frailty was defined using claims-based frailty index ≥0.25. We conducted multivariable logistic regression to identify factors associated with using potent P2Y12 inhibitors and multivariable-adjusted competing risk analyses to compare the rate of discontinuation of potent P2Y12 inhibitors in frail versus non-frail patients. There were 11,239 patients (mean age 74 years, 39% women). The prevalence of cardiovascular and geriatric co-morbidities was as follows: 32% chronic kidney disease, 28% heart failure, 10% previous myocardial infarction, 6% dementia, 20% anemia, and 12% frailty. The proportion of patients receiving clopidogrel decreased from 78.3% in 2010 to 2013 to 42.1% in 2018 to 2020, with a concurrent increase in those receiving potent P2Y12 inhibitors (mostly ticagrelor) from 21.7% to 57.9%. Frailty was independently associated with reduced odds of initiation (odds ratio 0.78, 95% confidence interval 0.67 to 0.90) but not with discontinuation of potent P2Y12 inhibitors (subdistribution hazard ratio 1.09, 95% confidence interval 0.98 to 1.22). In conclusion, frail older adults are less likely to receive potent P2Y12 inhibitors after percutaneous coronary intervention-treated ST-elevation myocardial infarction, but they are as likely as non-frail patients to continue with the prescribed P2Y12 inhibitor.

A Paratope-Enhanced Method to Determine Breadth and Depth TCR Clonal Metrics of the Private Human T-Cell Vaccine Response after SARS-CoV-2 Vaccination.

Quantitative metrics for vaccine-induced T-cell responses are an important need for developing correlates of protection and their use in vaccine-based medical management and population health. Molecular TCR analysis is an appealing strategy but currently requires a targeted methodology involving complex integration of ex vivo data (antigen-specific functional T-cell cytokine responses and TCR molecular responses) that uncover only public antigen-specific metrics. Here, we describe an untargeted private TCR method that measures breadth and depth metrics of the T-cell response to vaccine challenge using a simple pre- and post-vaccine subject sampling, TCR immunoseq analysis, and a bioinformatic approach using self-organizing maps and GLIPH2. Among 515 subjects undergoing SARS-CoV-2 mRNA vaccination, we found that breadth and depth metrics were moderately correlated between the targeted public TCR response and untargeted private TCR response methods. The untargeted private TCR method was sufficiently sensitive to distinguish subgroups of potential clinical significance also observed using public TCR methods (the reduced T-cell vaccine response with age and the paradoxically elevated T-cell vaccine response of patients on anti-TNF immunotherapy). These observations suggest the promise of this untargeted private TCR method to produce T-cell vaccine-response metrics in an antigen-agnostic and individual-autonomous context.

A genome-wide association study identifies 41 loci associated with eicosanoid levels.

Eicosanoids are biologically active derivatives of polyunsaturated fatty acids with broad relevance to health and disease. We report a genome-wide association study in 8406 participants of the Atherosclerosis Risk in Communities Study, identifying 41 loci associated with 92 eicosanoids and related metabolites. These findings highlight loci required for eicosanoid biosynthesis, including FADS1-3, ELOVL2, and numerous CYP450 loci. In addition, significant associations implicate a range of non-oxidative lipid metabolic processes in eicosanoid regulation, including at PKD2L1/SCD and several loci involved in fatty acyl-CoA metabolism. Further, our findings highlight select clearance mechanisms, for example, through the hepatic transporter encoded by SLCO1B1. Finally, we identify eicosanoids associated with aspirin and non-steroidal anti-inflammatory drug use and demonstrate the substantial impact of genetic variants even for medication-associated eicosanoids. These findings shed light on both known and unknown aspects of eicosanoid metabolism and motivate interest in several gene-eicosanoid associations as potential functional participants in human disease.

Frailty and uptake of angiotensin receptor neprilysin inhibitor for heart failure with reduced ejection fraction.

BACKGROUND: Frail older adults may be less likely to receive guideline-directed medical therapy (GDMT)-renin-angiotensin blockers, beta-blockers, and mineralocorticoid receptor antagonists-for heart failure with reduced ejection fraction (HFrEF). We aimed to examine the uptake of angiotensin receptor neprilysin inhibitor (ARNI) and GDMT in frail older adults with HFrEF. METHODS: Using 2015-2019 Medicare data, we estimated the proportion of beneficiaries with HFrEF receiving ARNI and GDMT each year by frailty status, defined by a claims-based frailty index. Logistic regression was used to identify clinical characteristics associated with ARNI initiation. Cox proportional hazards regression was used to examine the association of GDMT use in 2015 and death or heart failure hospitalization in 2016-2019. RESULTS: Among 147,506-180,386 beneficiaries with HFrEF (mean age: 77 years; 27% women; 42.6-49.1% frail) in 2015-2019, the proportion of patients receiving ARNI increased in both non-frail (0.4%-16.4%) and frail (0.3%-13.7%) patients (p for yearly-trend-by-frailty = 0.970). Among those not receiving a renin-angiotensin system blocker, patients with age ≥ 85 years (odds ratio [95% CI], 0.89 [0.80-0.99]), dementia (0.88 [0.81-0.96]), and frailty (0.87 [0.81-0.94]) were less likely to initiate ARNI. The proportion of patients receiving all 3 GDMT classes increased in non-frail patients (22.0%-27.0%) but changed minimally in frail patients (19.6%-21.8%). Regardless of frailty status, treatment with at least 1 class of GDMT was associated with lower death or heart failure hospitalization than no GDMT medications (hazard ratio [95% CI], 0.94 [0.91-0.97], 0.92 [0.89-0.94], 0.94 [0.91-0.97] for 1, 2, and 3 classes, respectively). CONCLUSIONS: Our results suggest an evidence-practice gap in the use of ARNI and GDMT in Medicare beneficiaries with HFrEF, particularly those with frailty. Efforts to narrow this gap are needed to reduce the burden of HFrEF in older adults.

Family History of Modifiable Risk Factors and Association With Future Cardiovascular Disease.

Background A parental history of cardiovascular disease (CVD) confers greater risk of future CVD among offspring. Whether the presence of parental modifiable risk factors contribute to or modify CVD risk in offspring is unclear. Methods and Results We studied 6278 parent-child trios in the multigenerational longitudinal Framingham Heart Study. We assessed parental history of CVD and modifiable risk factors (smoking, hypertension, diabetes, obesity, and hyperlipidemia). Multivariable Cox models were used to evaluate the association of parental history and future CVD among offspring. Among 6278 individuals (mean age 45±11 years), 44% had at least 1 parent with history of CVD. Over a median follow-up of 15 years, 353 major CVD events occurred among offspring. Parental history of CVD conferred 1.7-fold increased hazard of future CVD (hazard ratio [HR], 1.71 [95% CI, 1.33-2.21]). Parental obesity and smoking status were associated with higher hazard of future CVD (obesity: HR, 1.32 [95% CI, 1.06-1.64]; smoking: HR, 1.34 [95% CI, 1.07-1.68], attenuated after adjusting for offspring smoking status). By contrast, parental history of hypertension, diabetes, and hypercholesterolemia were not associated with future CVD in offspring (P>0.05 for all). Furthermore, parental risk factors did not modify the association of parental CVD history on future offspring CVD risk. Conclusions Parental history of obesity and smoking were associated with a higher hazard of future CVD in offspring. By contrast, other parental modifiable risk factors did not alter offspring CVD risk. In addition to parental CVD, the presence of parental obesity should prompt a focus on disease prevention.

The Association of Protein Biomarkers With Incident Heart Failure With Preserved and Reduced Ejection Fraction.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) are distinct clinical entities, yet there is scant evidence for associations of proteomic signatures with future development of HFpEF versus HFrEF. METHODS: We evaluated the association of 71 protein biomarkers with incident HFpEF versus HFrEF (left ventricular ejection fraction ≥ versus <50%) among Framingham Heart Study participants using multivariable Cox models. RESULTS: Among 7038 participants (mean age 49 years; 54% women), 5 biomarkers were associated with increased risk of incident HFpEF (false discovery rate q<0.05): NT-proBNP (N-terminal pro-B-type natriuretic peptide; hazard ratio [HR], 2.13; 95% CI, 1.52-2.99; P<0.001), growth differentiation factor-15 (HR, 1.67; 95% CI, 1.32-2.12; P<0.001), adrenomedullin (HR, 1.58; 95% CI, 1.23-2.04; P<0.001), uncarboxylated matrix Gla protein (HR, 1.55; 95% CI 1.23-1.95; P<0.001), and C-reactive protein (HR, 1.46; 95% CI, 1.17-1.83; P=0.001). Fourteen biomarkers were associated with incident HFrEF (multivariable P<0.001, q<0.05 for all). Of these, 3 biomarkers were associated with both HF subtypes (NT-proBNP, growth differentiation factor-15, and C-reactive protein). When compared directly, myeloperoxidase, resistin, and paraoxanase-1 were more strongly associated with HFrEF than HFpEF. CONCLUSIONS: We identified 5 protein biomarkers of new-onset HFpEF representing pathways of inflammation, cardiac stress, and vascular stiffness, which partly overlapped with HFrEF. We found 14 biomarkers associated with new-onset HFrEF, with some distinct associations including myeloperoxidase, resistin, and paraoxanase-1. Taken together, these findings provide insights into similarities and differences in the development of HF subtypes. REGISTRATION: URL: https://clinicaltrials.gov/ct2/show/NCT00005121; Unique identifier: NCT0005121.

The association between eicosanoids and incident atrial fibrillation in the Framingham Heart Study.

Chronic inflammation is a continuous low-grade activation of the systemic immune response. Whereas downstream inflammatory markers are associated with atrial fibrillation (AF), upstream inflammatory effectors including eicosanoids are less studied. To examine the association between eicosanoids and incident AF. We used a liquid chromatography-mass spectrometry for the non-targeted measurement of 161 eicosanoids and eicosanoid-related metabolites in the Framingham Heart Study. The association of each eicosanoid and incident AF was assessed using Cox proportional hazards models and adjusted for AF risk factors, including age, sex, height, weight, systolic/diastolic blood pressure, current smoking, antihypertensive medication, diabetes, history of myocardial infarction and heart failure. False discovery rate (FDR) was used to adjust for multiple testing. Eicosanoids with FDR < 0.05 were considered significant. In total, 2676 AF-free individuals (mean age 66 ± 9 years, 56% females) were followed for mean 10.8 ± 3.4 years; 351 participants developed incident AF. Six eicosanoids were associated with incident AF after adjusting for multiple testing (FDR < 0.05). A joint score was built from the top eicosanoids weighted by their effect sizes, which was associated with incident AF (HR = 2.72, CI = 1.71-4.31, P = 2.1 × 10-5). In conclusion, six eicosanoids were associated with incident AF after adjusting for clinical risk factors for AF.

Association of Physical Activity With Bioactive Lipids and Cardiovascular Events.

BACKGROUND: To clarify the mechanisms underlying physical activity (PA)-related cardioprotection, we examined the association of PA with plasma bioactive lipids (BALs) and cardiovascular disease (CVD) events. We additionally performed genome-wide associations. METHODS: PA-bioactive lipid associations were examined in VITAL (VITamin D and OmegA-3 TriaL)-clinical translational science center (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT01169259; N=1032) and validated in JUPITER (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin)-NC (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT00239681; N=589), using linear models adjusted for age, sex, race, low-density lipoprotein-cholesterol, total-C, and smoking. Significant BALs were carried over to examine associations with incident CVD in 2 nested CVD case-control studies: VITAL-CVD (741 case-control pairs) and JUPITER-CVD (415 case-control pairs; validation). RESULTS: We detected 145 PA-bioactive lipid validated associations (false discovery rate <0.1). Annotations were found for 6 of these BALs: 12,13-diHOME, 9,10-diHOME, lysoPC(15:0), oxymorphone-3b-D-glucuronide, cortisone, and oleoyl-glycerol. Genetic analysis within JUPITER-NC showed associations of 32 PA-related BALs with 22 single-nucleotide polymorphisms. From PA-related BALs, 12 are associated with CVD. CONCLUSIONS: We identified a PA-related bioactive lipidome profile out of which 12 BALs also had opposite associations with incident CVD events.

Cardiac microstructural alterations measured by echocardiography identify sex-specific risk for heart failure.

OBJECTIVE: Established preclinical imaging assessments of heart failure (HF) risk are based on macrostructural cardiac remodelling. Given that microstructural alterations may also influence HF risk, particularly in women, we examined associations between microstructural alterations and incident HF. METHODS: We studied N=2511 adult participants (mean age 65.7±8.8 years, 56% women) of the Framingham Offspring Study who were free of cardiovascular disease at baseline. We employed texture analysis of echocardiography to quantify microstructural alteration, based on the high spectrum signal intensity coefficient (HS-SIC). We examined its relations to incident HF in sex-pooled and sex-specific Cox models accounting for traditional HF risk factors and macrostructural alterations. RESULTS: We observed 94 new HF events over 7.4±1.7 years. Individuals with higher HS-SIC had increased risk for incident HF (HR 1.67 per 1-SD in HS-SIC, 95% CI 1.31 to 2.13; p<0.0001). Adjusting for age and antihypertensive medication use, this association was significant in women (p=0.02) but not men (p=0.78). Adjusting for traditional risk factors (including body mass index, total/high-density lipoprotein cholesterol, blood pressure traits, diabetes and smoking) attenuated the association in women (HR 1.30, p=0.07), with mediation of HF risk by the HS-SIC seen for a majority of these risk factors. However, the HS-SIC association with HF in women remained significant after adjusting for relative wall thickness (representing macrostructure alteration) in addition to these risk factors (HR 1.47, p=0.02). CONCLUSIONS: Cardiac microstructural alterations are associated with elevated risk for HF, particularly in women. Microstructural alteration may identify sex-specific pathways by which individuals progress from risk factors to clinical HF.

Association of Cardiometabolic Disease With Cancer in the Community.

Background: Obesity and cardiometabolic dysfunction have been associated with cancer risk and severity. Underlying mechanisms remain unclear. Objectives: The aim of this study was to examine associations of obesity and related cardiometabolic traits with incident cancer. Methods: FHS (Framingham Heart Study) and PREVEND (Prevention of Renal and Vascular End-Stage Disease) study participants without prevalent cancer were studied, examining associations of obesity, body mass index (BMI), waist circumference, visceral adipose tissue (VAT) and subcutaneous adipose tissue depots, and C-reactive protein (CRP) with future cancer in Cox models. Results: Among 20,667 participants (mean age 50 years, 53% women), 2,619 cancer events were observed over a median follow-up duration of 15 years. Obesity was associated with increased risk for future gastrointestinal (HR: 1.30; 95% CI: 1.05-1.60), gynecologic (HR: 1.62; 95% CI: 1.08-2.45), and breast (HR: 1.32; 95% CI: 1.05-1.66) cancer and lower risk for lung cancer (HR: 0.62; 95% CI: 0.44-0.87). Similarly, waist circumference was associated with increased risk for overall, gastrointestinal, and gynecologic but not lung cancer. VAT but not subcutaneous adipose tissue was associated with risk for overall cancer (HR: 1.22; 95% CI: 1.05-1.43), lung cancer (HR: 1.92; 95% CI: 1.01-3.66), and melanoma (HR: 1.56; 95% CI: 1.02-2.38) independent of BMI. Last, higher CRP levels were associated with higher risk for overall, colorectal, and lung cancer (P < 0.05 for all). Conclusions: Obesity and abdominal adiposity are associated with future risk for specific cancers (eg, gastrointestinal, gynecologic). Although obesity was associated with lower risk for lung cancer, greater VAT and CRP were associated with higher lung cancer risk after adjusting for BMI.

Cardiac microstructural alterations in immune-inflammatory myocardial disease: a retrospective case-control study.

BACKGROUND: Immune-inflammatory myocardial disease contributes to multiple chronic cardiac processes, but access to non-invasive screening is limited. We have previously developed a method of echocardiographic texture analysis, called the high-spectrum signal intensity coefficient (HS-SIC) which assesses myocardial microstructure and previously associated with myocardial fibrosis. We aimed to determine whether this echocardiographic texture analysis of cardiac microstructure can identify inflammatory cardiac disease in the clinical setting. METHODS: We conducted a retrospective case-control study of 318 patients with distinct clinical myocardial pathologies and 20 healthy controls. Populations included myocarditis, atypical chest pain/palpitations, STEMI, severe aortic stenosis, acute COVID infection, amyloidosis, and cardiac transplantation with acute rejection, without current rejection but with prior rejection, and with no history of rejection. We assessed the HS-SIC's ability to differentiate between a broader diversity of clinical groups and healthy controls. We used Kruskal-Wallis tests to compare HS-SIC values measured in each of the clinical populations with those in the healthy control group and compared HS-SIC values between the subgroups of cardiac transplantation rejection status. RESULTS: For the total sample of N = 338, the mean age was 49.6 ± 20.9 years and 50% were women. The mean ± standard error of the mean of HS-SIC were: 0.668 ± 0.074 for controls, 0.552 ± 0.049 for atypical chest pain/palpitations, 0.425 ± 0.058 for myocarditis, 0.881 ± 0.129 for STEMI, 1.116 ± 0.196 for severe aortic stenosis, 0.904 ± 0.116 for acute COVID, and 0.698 ± 0.103 for amyloidosis. Among cardiac transplant recipients, HS-SIC values were 0.478 ± 0.999 for active rejection, 0.594 ± 0.091 for prior rejection, and 1.191 ± 0.442 for never rejection. We observed significant differences in HS-SIC between controls and myocarditis (P = 0.0014), active rejection (P = 0.0076), and atypical chest pain or palpitations (P = 0.0014); as well as between transplant patients with active rejection and those without current or prior rejection (P = 0.031). CONCLUSIONS: An echocardiographic method can be used to characterize tissue signatures of microstructural changes across a spectrum of cardiac disease including immune-inflammatory conditions.

The T-Cell Response to SARS-CoV-2 Vaccination in Inflammatory Bowel Disease is Augmented with Anti-TNF Therapy.

T-cell and antibody responses to severe acute respiratory syndrome coronavirus 2 vaccination in inflammatory bowel disease patients are poorly correlated. T-cell responses are preserved by most biologic therapies, but augmented by anti-tumor necrosis factor (anti-TNF) treatment. While anti-TNF therapy blunts the antibody response, cellular immunity after vaccination is robust.

Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort.

Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP-taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host-microbiota interactions and their association with disease.

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning.

IMPORTANCE: Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis. OBJECTIVE: To assess the accuracy of a deep learning workflow in quantifying ventricular hypertrophy and predicting the cause of increased LV wall thickness. DESIGN, SETTINGS, AND PARTICIPANTS: This cohort study included physician-curated cohorts from the Stanford Amyloid Center and Cedars-Sinai Medical Center (CSMC) Advanced Heart Disease Clinic for cardiac amyloidosis and the Stanford Center for Inherited Cardiovascular Disease and the CSMC Hypertrophic Cardiomyopathy Clinic for hypertrophic cardiomyopathy from January 1, 2008, to December 31, 2020. The deep learning algorithm was trained and tested on retrospectively obtained independent echocardiogram videos from Stanford Healthcare, CSMC, and the Unity Imaging Collaborative. MAIN OUTCOMES AND MEASURES: The main outcome was the accuracy of the deep learning algorithm in measuring left ventricular dimensions and identifying patients with increased LV wall thickness diagnosed with hypertrophic cardiomyopathy and cardiac amyloidosis. RESULTS: The study included 23 745 patients: 12 001 from Stanford Health Care (6509 [54.2%] female; mean [SD] age, 61.6 [17.4] years) and 1309 from CSMC (808 [61.7%] female; mean [SD] age, 62.8 [17.2] years) with parasternal long-axis videos and 8084 from Stanford Health Care (4201 [54.0%] female; mean [SD] age, 69.1 [16.8] years) and 2351 from CSMS (6509 [54.2%] female; mean [SD] age, 69.6 [14.7] years) with apical 4-chamber videos. The deep learning algorithm accurately measured intraventricular wall thickness (mean absolute error [MAE], 1.2 mm; 95% CI, 1.1-1.3 mm), LV diameter (MAE, 2.4 mm; 95% CI, 2.2-2.6 mm), and posterior wall thickness (MAE, 1.4 mm; 95% CI, 1.2-1.5 mm) and classified cardiac amyloidosis (area under the curve [AUC], 0.83) and hypertrophic cardiomyopathy (AUC, 0.98) separately from other causes of LV hypertrophy. In external data sets from independent domestic and international health care systems, the deep learning algorithm accurately quantified ventricular parameters (domestic: R2, 0.96; international: R2, 0.90). For the domestic data set, the MAE was 1.7 mm (95% CI, 1.6-1.8 mm) for intraventricular septum thickness, 3.8 mm (95% CI, 3.5-4.0 mm) for LV internal dimension, and 1.8 mm (95% CI, 1.7-2.0 mm) for LV posterior wall thickness. For the international data set, the MAE was 1.7 mm (95% CI, 1.5-2.0 mm) for intraventricular septum thickness, 2.9 mm (95% CI, 2.4-3.3 mm) for LV internal dimension, and 2.3 mm (95% CI, 1.9-2.7 mm) for LV posterior wall thickness. The deep learning algorithm accurately detected cardiac amyloidosis (AUC, 0.79) and hypertrophic cardiomyopathy (AUC, 0.89) in the domestic external validation site. CONCLUSIONS AND RELEVANCE: In this cohort study, the deep learning model accurately identified subtle changes in LV wall geometric measurements and the causes of hypertrophy. Unlike with human experts, the deep learning workflow is fully automated, allowing for reproducible, precise measurements, and may provide a foundation for precision diagnosis of cardiac hypertrophy.

Sex Differences in Peripheral Artery Disease.

Peripheral artery disease (PAD) is a prevalent condition that confers substantial morbidity and mortality and remains underdiagnosed as well as undertreated in the overall population. Although PAD prevalence is similar or higher in women compared with men, associations of traditional and nontraditional risk factors with PAD and clinical manifestations of PAD differ by sex and may contribute to delayed or lack of diagnosis in women. Such sex-based differences in the manifestation of PAD may arise from sexual dimorphism in the vascular substrate in health as well as sex variation in the responses to vascular stressors. Despite the availability of proven therapies for improving symptoms and reducing risk of ischemic cardiovascular and limb events among patients with diagnosed PAD, important sex differences in treatment and outcomes have been observed. We provide an overview of current knowledge regarding sex differences in the epidemiology, pathophysiology, clinical presentation, and management of PAD.