Cytokine signature clusters as a tool to compare changes associated with tobacco product use in upper and lower airway samples.

Inhalation exposure to cigarette smoke and e-cigarette aerosol is known to alter the respiratory immune system, particularly cytokine signaling. In assessments of health impacts of tobacco product use, cytokines are often measured using a variety of sample types, from serum to airway mucosa. However, it is currently unclear whether and how well cytokine levels from different sample types and the airway locations they represent are correlated, making comparing studies that utilize differing sample types challenging. To address this challenge, we compared baseline cytokine signatures in upper and lower airways and systemic samples and evaluated how groups of coexpressed cytokines change with tobacco product use. Matched nasal lavage fluid (NLF), nasal epithelial lining fluid (NELF), sputum, and circulating serum samples were collected from 14 nonsmokers, 13 cigarette smokers, and 17 e-cigarette users and analyzed for levels of 22 cytokines. Individual cytokine signatures were first compared across each sample type, followed by identification of cytokine clusters within each sample type. Identified clusters were then evaluated for potential alterations following tobacco product use using eigenvector analyses. Individual cytokine signatures in the respiratory tract were significantly correlated (NLF, NELF, and sputum) compared with randomly permutated signatures, whereas serum was not significantly different from random permutations. Cytokine clusters that were similar across airway sample types were modified by tobacco product use, particularly e-cigarettes, indicating a degree of uniformity in terms of how cytokine host defense and immune cell recruitment responses cooperate in the upper and lower airways. Overall, cluster-based analyses were found to be especially useful in small cohort assessments, providing higher sensitivity than individual signatures to detect biologically meaningful differences between tobacco use groups. This novel cluster analysis approach revealed that eigencytokine patterns in noninvasive upper airway samples simulate cytokine patterns in lower airways.

Inorganic arsenic and its methylated metabolites as endocrine disruptors in the placenta: Mechanisms underpinning glucocorticoid receptor (GR) pathway perturbations.

Exposure to inorganic arsenic (iAs) is a significant public health concern with individuals around the globe exposed to harmful levels through contaminated drinking water. Exposure to iAs during pregnancy is of particular concern and has been associated with pregnancy complications and adverse child health later in life. Effects of in utero exposure may be mediated through alterations in key signaling pathways in the placenta that regulate fetal growth and development. A pathway of interest is the glucocorticoid receptor (GR)- signaling pathway, which is known to regulate fetal and placental development. While prior research has shown that iAs alters GR-associated gene expression in trophoblasts, the mechanisms that underlie these perturbations remain unknown. In the present study, we set out to elucidate the molecular mechanisms that underpin observed alterations in GR-associated gene expression. We also aimed to determine whether the methylated metabolites of iAs, namely monomethyl­arsenic (MMA) and dimethyl­arsenic (DMA), also influence GR-associated signaling in the placenta. The data indicate that iAs alters GR activation in a dose-dependent manner, reduces nuclear translocation, and reduces DNA binding. Additionally, the results demonstrate that MMA and DMA alter the expression of eight GR-associated genes, modulate GR activation, and alter DNA binding. These data are significant as they highlight the role of iAs as an endocrine disruptor and for the first time explore the effects of MMA and DMA on endocrine signaling in the placenta.

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach.

BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide. OBJECTIVE: The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features. METHODS: Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India). RESULTS: ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%. CONCLUSION: In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.

Effects of Wildfire Events on California Radiation Oncology Clinics and Patients.

Purpose: The effect of climate-driven events, such as wildfires, on health care delivery and cancer care is a growing concern. Patients with cancer undergoing radiation therapy are particularly vulnerable to treatment interruptions, which have a direct effect on survival. We report the results of a study characterizing the effect of wildfires on radiation oncology clinics and their patients. Methods and Materials: A survey of California radiation oncologists was used to evaluate emergency preparedness and the effect of wildfires on the delivery of radiation therapy services between 2017 and 2022. Descriptive statistics and Pearson's χ2 tests were performed to investigate potential relationships between provider characteristics, practice settings, and perceptions of the effect of wildfire events. California Department of Forestry and Fire Protection data were employed to map the geographic distribution of wildfires to clinic locations. Results: Response rate was 12.3% (51/415 radiation oncologists), representing 25% of clinics (43/176) in 41% (24/58) of California counties. Sixty-one percent (31/51) of respondents reported being affected by a wildfire, 2 of which are rural clinics (100%, 2/2) and 29 are (59%, 29/49) metropolitan practices. Of these, 18% (9/51) reported a clinic closure, and 29% (15/51) reported staffing shortages. Respondents reported effects on patients, including having to evacuate (55%, 28/51), cancel/reschedule treatments (53%, 27/51), and experiencing physical, mental, or financial hardship due to wildfires (45%, 23/51). Respondents described effects on clinical operations, including being forced to transfer patients (24%, 12/51), transportation interruptions (37%, 19/51), regional/community evacuations (35%, 18/51), and physical/mental health effects (27%, 14/51) on clinic personnel. Less than half of the respondents (47%, 24/51) reported their workplace had a wildfire emergency preparedness plan. Additionally, geographic analysis revealed that 100% (176/176) of clinics were located within 25 miles of a wildfire. Conclusions: This study highlights the effects of wildfires on radiation oncology clinics and patients and underscores the need for emergency preparedness planning to minimize the consequences of such disasters.

SGLT2 Inhibitor Use and Risk of Clinical Events in Patients With Cancer Therapy-Related Cardiac Dysfunction.

BACKGROUND: Certain antineoplastic therapies are associated with an increased risk of cardiomyopathy and heart failure (HF). Sodium glucose co-transporter 2 (SGLT2) inhibitors improve outcomes in patients with HF. OBJECTIVES: This study aims to examine the efficacy of SGLT2 inhibitors in patients with cancer therapy-related cardiac dysfunction (CTRCD) or HF. METHODS: The authors conducted a retrospective cohort analysis of deidentified, aggregate patient data from the TriNetX research network. Patients aged ≥18 years with a history of type 2 diabetes mellitus, cancer, and exposure to potentially cardiotoxic antineoplastic therapies, with a subsequent diagnosis of cardiomyopathy or HF between January 1, 2013, and April 30, 2020, were identified. Patients with ischemic heart disease were excluded. Patients receiving guideline-directed medical therapy were divided into 2 groups based on SGLT2 inhibitor use. After propensity score matching, odds ratios (ORs) and Cox proportional HRs were used to compare outcomes over a 2-year follow-up period. RESULTS: The study cohort included 1,280 patients with CTRCD/HF (n = 640 per group; mean age: 67.6 years; 41.6% female; 68% White). Patients on SGLT2 inhibitors in addition to conventional guideline-directed medical therapy had a lower risk of acute HF exacerbation (OR: 0.483 [95% CI: 0.36-0.65]; P < 0.001) and all-cause mortality (OR: 0.296 [95% CI: 0.22-0.40]; P = 0.001). All-cause hospitalizations or emergency department visits (OR: 0.479; 95% CI: 0.383-0.599; P < 0.001), atrial fibrillation/flutter (OR: 0.397 [95% CI: 0.213-0.737]; P = 0.003), acute kidney injury (OR: 0.486 [95% CI: 0.382-0.619]; P < 0.001), and need for renal replacement therapy (OR: 0.398 [95% CI: 0.189-0.839]; P = 0.012) were also less frequent in patients on SGLT2 inhibitors. CONCLUSIONS: SGLT2 inhibitor use is associated with improved outcomes in patients with CTRCD/HF.

Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review.

BACKGROUND: The electrocardiogram (ECG) is one of the most common diagnostic tools available to assess cardio-vascular health. The advent of advanced computational techniques such as deep learning has dramatically expanded the breadth of clinical problems that can be addressed using ECG data, leading to increasing popularity of ECG deep-learning models aimed at predicting clinical endpoints. OBJECTIVES: The purpose of this study was to define the current landscape of clinically relevant ECG deep-learning models and examine practices in the scientific reporting of these studies. METHODS: We performed a systematic review of PubMed and EMBASE databases to identify clinically relevant ECG deep-learning models published through July 1, 2022. RESULTS: We identified 44 manuscripts including 53 unique, clinically relevant ECG deep-learning models. The rate of publication of ECG deep-learning models is increasing rapidly. The most common clinical applications of ECG deep learning were identification of cardiomyopathy (14/53 [26%]), followed by arrhythmia detection (9/53 [17%]). Methodologic reporting varied; while 33/44 (75%) publications included model architecture diagrams, complete information required to reproduce these models was provided in only 10/44 (23%). Saliency analysis was performed in 20/44 (46%) of publications. Only 18/53 (34%) models were tested within external validation cohorts. Model code or resources allowing for model implementation by external groups were available for only 5/44 (11%) publications. CONCLUSIONS: While ECG deep-learning models are increasingly clinically relevant, their reporting is highly variable, and few publications provide sufficient detail for methodologic reproduction or model validation by external groups. The field of ECG deep learning would benefit from adherence to a set of standardized scientific reporting guidelines.

Psychosocial Stressors as a Determinant of Maternal Cardiovascular Health During Pregnancy.

Maternal psychosocial stress may be a risk factor for poor cardiovascular health (CVH) during pregnancy. We aimed to identify classes of psychosocial stressors in pregnant women and to evaluate their cross-sectional association with CVH. We performed a secondary analysis of women from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b) cohort (2010 to 2013). Latent class analysis was used to identify distinct classes of exposure to psychosocial stressors based on psychological (stress, anxiety, resilience, depression) and sociocultural indicators (social support, economic stress, discrimination). Optimal and suboptimal CVH was defined based on the presence of 0 to 1 and ≥2 risk factors (hypertension, diabetes mellitus, smoking, obesity, inadequate physical activity), respectively based on the American Heart Association Life's Essential 8. We used logistic regression to evaluate the association between psychosocial classes and CVH. We included 8,491 women and identified 5 classes reflective of gradations of psychosocial stress. In unadjusted models, women in the most disadvantaged psychosocial stressor class were approximately 3 times more likely to have suboptimal CVH than those in the most advantaged class (odds ratio 2.98, 95% confidence interval: 2.54 to 3.51). Adjusting for demographics minimally attenuated the risk (adjusted odds ratio 2.09, 95% confidence interval: 1.76 to 2.48). We observed variation across psychosocial stressor landscapes in women in the nuMoM2b cohort. Women in the most disadvantaged psychosocial class had a greater risk of suboptimal CVH which was only partially explained by differences in demographic characteristics. In conclusion, our findings highlight the association of maternal psychosocial stressors with CVH during pregnancy.

Incidence, treatment and outcomes of coronary artery dissection during percutaneous coronary intervention.

BACKGROUND: Coronary artery dissection is a feared and potentially life-threatening complication of percutaneous coronary intervention (PCI). METHODS: We examined the clinical, angiographic, and procedural characteristics, and outcomes of coronary dissection at a tertiary care institution. RESULTS: Between 2014 and 2019, unplanned coronary dissection occurred in 141 of 10,278 PCIs (1.4%). Median patient age was 68 (60, 78) years, 68% were men, and 83% had hypertension. The prevalence of diabetes (29%), and prior PCI (37%) was high. Most target vessels were significantly diseased: 48% had moderate/severe tortuosity and 62% had moderate/severe calcification. The most common cause of dissection was guidewire advancement (30%), followed by stenting (22%), balloon angioplasty (20%), and guide-catheter engagement (18%). TIMI flow was 0 in 33% and 1-2 in 41% of cases. Intravascular imaging was used in 17% of the cases. Stenting was used to treat the dissection in 73% of patients. There was no consequence of dissection in 43% of patients. Technical and procedural success was 65% and 55%, respectively. In-hospital major adverse cardiovascular events occurred in 23% of patients: 13 (9%) had an acute myocardial infarction (MI), 3 (2%) had emergency coronary artery bypass graft surgery, and 10 (7%) died. During a mean follow up of 1612 days, 28 (20%) patients died, and the rate of target lesion revascularization was 11.3% (n=16). CONCLUSION: Coronary artery dissection is an infrequent complication of PCI, but is associated with adverse clinical outcomes, such as death and acute MI.

Venoarterial Extracorporeal Membrane Oxygenation for Life-Threatening Complications of Percutaneous Coronary and Structural Heart Interventions.

Percutaneous coronary or structural heart interventions may lead to complications, such as perforation and acute vessel closure that may in turn lead to cardiac arrest or cardiogenic shock. Moreover, acute coronary syndrome patients presenting with cardiogenic shock can be challenging to treat due to hemodynamic instability. In such cases, venoarterial extracorporeal membrane oxygenation (V-A ECMO) can provide hemodynamic stabilization and oxygenation allowing successful treatment of the complication or culprit lesion in acute coronary syndrome patients. We present 3 cases illustrating successful emergent use of V-A ECMO in the cardiac catheterization laboratory in the setting of acute left main dissection during a chronic total occlusion intervention, cardiogenic shock in the setting of non-ST segment elevation myocardial infarction and multivessel coronary artery disease, and aortic annular rupture during transcatheter aortic valve replacement.

Placental genomics mediates genetic associations with complex health traits and disease.

As the master regulator in utero, the placenta is core to the Developmental Origins of Health and Disease (DOHaD) hypothesis but is historically understudied. To identify placental gene-trait associations (GTAs) across the life course, we perform distal mediator-enriched transcriptome-wide association studies (TWAS) for 40 traits, integrating placental multi-omics from the Extremely Low Gestational Age Newborn Study. At [Formula: see text], we detect 248 GTAs, mostly for neonatal and metabolic traits, across 176 genes, enriched for cell growth and immunological pathways. In aggregate, genetic effects mediated by placental expression significantly explain 4 early-life traits but no later-in-life traits. 89 GTAs show significant mediation through distal genetic variants, identifying hypotheses for distal regulation of GTAs. Investigation of one hypothesis in human placenta-derived choriocarcinoma cells reveal that knockdown of mediator gene EPS15 upregulates predicted targets SPATA13 and FAM214A, both associated with waist-hip ratio in TWAS, and multiple genes involved in metabolic pathways. These results suggest profound health impacts of placental genomic regulation in developmental programming across the life course.

Incidence, Treatment, and Outcomes of Coronary Artery Perforation During Percutaneous Coronary Intervention.

OBJECTIVES: To examine the incidence, treatment and outcomes of perforation during percutaneous coronary intervention (PCI). BACKGROUND: Coronary perforation is a potentially life-threatening PCI complication. METHODS: We examined the clinical, angiographic, and procedural characteristics, management, and outcomes of coronary perforation at a tertiary care institution. RESULTS: Between 2014 and 2019, perforation occurred in 70 of 10,278 PCIs (0.7%). Patient age was 71 ± 12 years, 66% were men, and 30% had prior coronary artery bypass graft surgery. Among perforation cases, the prevalence of chronic total occlusions was 33%, moderate/severe calcification was 66% and moderate/severe tortuosity was 41%. The frequency of Ellis class 1, 2, and 3 perforations was 14%, 50%, and 36%, respectively. Most (n = 51; 73%) were large vessel perforations, 16 (23%) were distal vessel perforations and 3 (4%) were collateral vessel perforations (1 septal and 2 epicardial). Hypotension occurred in 26%, pericardial effusion in 36% and tamponade in 13%; 47% of perforations did not have clinical consequences. Perforations were most often treated with prolonged balloon inflation (63%), reversal of anticoagulation (39%), and covered stent implantation (33%). Technical and procedural success were 73% and 60%, respectively, and major periprocedural adverse cardiac events occurred in 21% of the patients. Three patients (4%) required emergent CABG surgery and four (6%) died. CONCLUSIONS: Coronary perforation is an infrequent complication of PCI. Most perforations are large vessel perforations and often require further intervention. The incidence of death or emergent cardiac surgery is low.

Associations of exposure to perfluoroalkyl substances individually and in mixtures with persistent infections: Recent findings from NHANES 1999-2016.

Certain viruses and parasites can cause persistent infections that often co-occur and have been associated with substantial morbidity and mortality. Separate lines of research indicate exposures to per- and polyfluoroalkyl substances (PFAS) suppress the immune system. We hypothesized that PFAS exposures might systematically increase susceptibility to persistent infections resulting in a higher pathogen burden. We used data from 8778 individuals (3189 adolescents, 5589 adults) in the nationally-representative U.S. National Health and Nutrition Examination Survey (NHANES) 1999-2016 to examine cross-sectional associations between serum concentrations of four highly detected PFAS (PFOS, PFOA, PFHxS, PFNA) with the presence of antibodies to cytomegalovirus, Epstein Barr virus, hepatitis C and E, herpes simplex 1 and 2, HIV, T. gondii, and Toxocara spp. Seropositivity was summed to calculate a pathogen burden score reflecting the total number of infections. Separate survey-weighted multivariable regression models were fitted to analyze PFAS individually and quantile g-computation was used to analyze PFAS mixtures. Among adolescents, 38.7% had at least one persistent infection while 14.9% had two or more; among adults, these percentages were 48.0% and 19.7%. Each PFAS was individually associated with significantly higher pathogen burdens and the most pronounced associations were observed in adolescents [e.g., among adolescents, a doubling of PFOS was associated with 30% (95% CI: 25-36%) higher pathogen burden]. Quantile g-computation revealed PFAS mixtures as a whole were also associated with higher pathogen burdens. Taken together, these results suggest PFAS exposure may increase susceptibility to and foster the clustering of persistent infections, particularly among adolescents. Since persistent infections are important contributors to long-term health, prospective data are needed to confirm these findings.

Mixtures modeling identifies chemical inducers versus repressors of toxicity associated with wildfire smoke.

Exposure to wildfire smoke continues to be a growing threat to public health, yet the chemical components in wildfire smoke that primarily drive toxicity and associated disease are largely unknown. This study utilized a suite of computational approaches to identify groups of chemicals induced by variable biomass burn conditions that were associated with biological responses in the mouse lung, including pulmonary immune response and injury markers. Smoke condensate samples were collected and characterized, resulting in chemical distribution information for 86 constituents across ten different exposures. Mixtures-relevant statistical methods included (i) a chemical clustering and data-reduction method, weighted chemical co-expression network analysis (WCCNA), (ii) a quantile g-computation approach to address the joint effect of multiple chemicals in different groupings, and (iii) a correlation analysis to compare mixtures modeling results against individual chemical relationships. Seven chemical groups were identified using WCCNA based on co-occurrence showing both positive and negative relationships with biological responses. A group containing methoxyphenols (e.g., coniferyl aldehyde, eugenol, guaiacol, and vanillin) displayed highly significant, negative relationships with several biological responses, including cytokines and lung injury markers. This group was further shown through quantile g-computation methods to associate with reduced biological responses. Specifically, mixtures modeling based on all chemicals excluding those in the methoxyphenol group demonstrated more significant, positive relationships with several biological responses; whereas mixtures modeling based on just those in the methoxyphenol group demonstrated significant negative relationships with several biological responses, suggesting potential protective effects. Mixtures-based analyses also identified other groups consisting of inorganic elements and ionic constituents showing positive relationships with several biological responses, including markers of inflammation. Many of the effects identified through mixtures modeling in this analysis were not captured through individual chemical analyses. Together, this study demonstrates the utility of mixtures-based approaches to identify potential drivers and inhibitors of toxicity relevant to wildfire exposures.

Comparing the Predictivity of Human Placental Gene, microRNA, and CpG Methylation Signatures in Relation to Perinatal Outcomes.

Molecular signatures are being increasingly integrated into predictive biology applications. However, there are limited studies comparing the overall predictivity of transcriptomic versus epigenomic signatures in relation to perinatal outcomes. This study set out to evaluate mRNA and microRNA (miRNA) expression and cytosine-guanine dinucleotide (CpG) methylation signatures in human placental tissues and relate these to perinatal outcomes known to influence maternal/fetal health; namely, birth weight, placenta weight, placental damage, and placental inflammation. The following hypotheses were tested: (1) different molecular signatures will demonstrate varying levels of predictivity towards perinatal outcomes, and (2) these signatures will show disruptions from an example exposure (ie, cadmium) known to elicit perinatal toxicity. Multi-omic placental profiles from 390 infants in the Extremely Low Gestational Age Newborns cohort were used to develop molecular signatures that predict each perinatal outcome. Epigenomic signatures (ie, miRNA and CpG methylation) consistently demonstrated the highest levels of predictivity, with model performance metrics including R2 (predicted vs observed) values of 0.36-0.57 for continuous outcomes and balanced accuracy values of 0.49-0.77 for categorical outcomes. Top-ranking predictors included miRNAs involved in injury and inflammation. To demonstrate the utility of these predictive signatures in screening of potentially harmful exogenous insults, top-ranking miRNA predictors were analyzed in a separate pregnancy cohort and related to cadmium. Key predictive miRNAs demonstrated altered expression in association with cadmium exposure, including miR-210, known to impact placental cell growth, blood vessel development, and fetal weight. These findings inform future predictive biology applications, where additional benefit will be gained by including epigenetic markers.

Perfluoroalkyl Substances (PFAS) and Their Effects on the Placenta, Pregnancy, and Child Development: a Potential Mechanistic Role for Placental Peroxisome Proliferator-Activated Receptors (PPARs).

PURPOSE OF REVIEW: This review summarizes studies highlighting perfluoroalkyl substances (PFAS) and their effects on the placenta, pregnancy outcomes, and child health. It highlights human population-based associations as well as in vitro-based experimental data to inform an understanding of the molecular mechanisms underlying these health effects. Among the mechanisms by which PFAS may induce toxicity is via their interaction with the peroxisome proliferator-activated receptors (PPARs), nuclear receptors that regulate lipid metabolism and placental functions important to healthy pregnancies, as well as fetal and child development. RECENT FINDINGS: In utero exposure to prevalent environmental contaminants such as PFAS is associated with negative health outcomes during pregnancy, birth outcomes, and later in life. Specifically, PFAS have been associated with increased incidence of gestational diabetes, childhood obesity, preeclampsia, and fetal growth restriction. In terms of placental molecular mechanisms underlying these associations, studies demonstrate that PFAS interfere with trophoblast lipid homeostasis, inflammation, and invasion. Moreover these effects could be mediated in part by the interaction between PFAS and PPARs, as well as other biological mechanisms. This review summarizes how PFAS, critical environmental contaminants, may contribute to diseases of pregnancy as well as early and later child health.