Life-long epigenetic programming of cortical architecture by maternal ‘Western’ diet during pregnancy

Funding: European Research Council (SECRET-CELLS, ERC-2015-AdG-695136; T.H.); Wellcome Trust grant number 094476/Z/10/Z, which funded the purchase of the TripleTOF 5600 mass spectrometer at the BSRC Mass Spectrometry and Proteomics Facility, University of St. Andrews.The evolution of human diets led to preferences toward polyunsaturated fatty acid (PUFA) content with ‘Western’ diets enriched in ω-6 PUFAs. Mounting evidence points to ω-6 PUFA excess limiting metabolic and cognitive processes that define longevity in humans. When chosen during pregnancy, ω-6 PUFA-enriched ‘Western’ diets can reprogram maternal bodily metabolism with maternal nutrient supply precipitating the body-wide imprinting of molecular and cellular adaptations at the level of long-range intercellular signaling networks in the unborn fetus. Even though unfavorable neurological outcomes are amongst the most common complications of intrauterine ω-6 PUFA excess, cellular underpinnings of life-long modifications to brain architecture remain unknown. Here, we show that nutritional ω-6 PUFA-derived endocannabinoids desensitize CB1 cannabinoid receptors, thus inducing epigenetic repression of transcriptional regulatory networks controlling neuronal differentiation. We found that cortical neurons lose their positional identity and axonal selectivity when mouse fetuses are exposed to excess ω-6 PUFAs in utero. Conversion of ω-6 PUFAs into endocannabinoids disrupted the temporal precision of signaling at neuronal CB1 cannabinoid receptors, chiefly deregulating Stat3-dependent transcriptional cascades otherwise required to execute neuronal differentiation programs. Global proteomics identified the immunoglobulin family of cell adhesion molecules (IgCAMs) as direct substrates, with DNA methylation and chromatin accessibility profiling uncovering epigenetic reprogramming at >1400 sites in neurons after prolonged cannabinoid exposure. We found anxiety and depression-like behavioral traits to manifest in adult offspring, which is consistent with genetic models of reduced IgCAM expression, to suggest causality for cortical wiring defects. Overall, our data uncover a regulatory mechanism whose disruption by maternal food choices could limit an offspring’s brain function for life.PostprintPeer reviewe

Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality

Variability independent of mean blood pressure as a real-world measure of cardiovascular risk

BackgroundIndividual-level blood pressure (BP) variability, independent of mean BP levels, has been associated with increased risk for cardiovascular events in cohort studies and clinical trials using standardized BP measurements. The extent to which BP variability relates to cardiovascular risk in the real-world clinical practice setting is unclear. We sought to determine if BP variability in clinical practice is associated with adverse cardiovascular outcomes using clinically generated data from the electronic health record (EHR).MethodsWe identified 42,482 patients followed continuously at a single academic medical center in Southern California between 2013 and 2019 and calculated their systolic and diastolic BP variability independent of the mean (VIM) over the first 3 years of the study period. We then performed multivariable Cox proportional hazards regression to examine the association between VIM and both composite and individual outcomes of interest (incident myocardial infarction, heart failure, stroke, and death).FindingsBoth systolic (HR, 95% CI 1.22, 1.17–1.28) and diastolic VIM (1.24, 1.19–1.30) were positively associated with the composite outcome, as well as all individual outcome measures. These findings were robust to stratification by age, sex and clinical comorbidities. In sensitivity analyses using a time-shifted follow-up period, VIM remained significantly associated with the composite outcome for both systolic (1.15, 1.11–1.20) and diastolic (1.18, 1.13–1.22) values.InterpretationVIM derived from clinically generated data remains associated with adverse cardiovascular outcomes and represents a risk marker beyond mean BP, including in important demographic and clinical subgroups. The demonstrated prognostic ability of VIM derived from non-standardized BP readings indicates the utility of this measure for risk stratification in a real-world practice setting, although residual confounding from unmeasured variables cannot be excluded.</p

Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors.

Messenger RNA encodes cellular function and phenotype. In the context of human cancer, it defines the identities of malignant cells and the diversity of tumor tissue. We studied 72,501 single-cell transcriptomes of human renal tumors and normal tissue from fetal, pediatric, and adult kidneys. We matched childhood Wilms tumor with specific fetal cell types, thus providing evidence for the hypothesis that Wilms tumor cells are aberrant fetal cells. In adult renal cell carcinoma, we identified a canonical cancer transcriptome that matched a little-known subtype of proximal convoluted tubular cell. Analyses of the tumor composition defined cancer-associated normal cells and delineated a complex vascular endothelial growth factor (VEGF) signaling circuit. Our findings reveal the precise cellular identities and compositions of human kidney tumors

From Romantic Gothic to Victorian Medievalism: 1817 and 1877

&quot;The Cambridge History of the Gothic was conceived in 2015, when Linda Bree, then Editorial Director at Cambridge University Press, first suggested the idea to us

High-Throughput Assessment of Real-World Medication Effects on QT Interval Prolongation: Observational Study

BackgroundDrug-induced prolongation of the corrected QT interval (QTc) increases the risk for Torsades de Pointes (TdP) and sudden cardiac death. Medication effects on the QTc have been studied in controlled settings but may not be well evaluated in real-world settings where medication effects may be modulated by patient demographics and comorbidities as well as the usage of other concomitant medications. ObjectiveWe demonstrate a new, high-throughput method leveraging electronic health records (EHRs) and the Surescripts pharmacy database to monitor real-world QTc-prolonging medication and potential interacting effects from demographics and comorbidities. MethodsWe included all outpatient electrocardiograms (ECGs) from September 2008 to December 2019 at a large academic medical system, which were in sinus rhythm with a heart rate of 40-100 beats per minute, QRS duration of <120 milliseconds, and QTc of 300-700 milliseconds, determined using the Bazett formula. We used prescription information from the Surescripts pharmacy database and EHR medication lists to classify whether a patient was on a medication during an ECG. Negative control ECGs were obtained from patients not currently on the medication but who had been or would be on that medication within 1 year. We calculated the difference in mean QTc between ECGs of patients who are on and those who are off a medication and made comparisons to known medication TdP risks per the CredibleMeds.org database. Using linear regression analysis, we studied the interaction of patient-level demographics or comorbidities on medication-related QTc prolongation. ResultsWe analyzed the effects of 272 medications on 310,335 ECGs from 159,397 individuals. Medications associated with the greatest QTc prolongation were dofetilide (mean QTc difference 21.52, 95% CI 10.58-32.70 milliseconds), mexiletine (mean QTc difference 18.56, 95% CI 7.70-29.27 milliseconds), amiodarone (mean QTc difference 14.96, 95% CI 13.52-16.33 milliseconds), rifaximin (mean QTc difference 14.50, 95% CI 12.12-17.13 milliseconds), and sotalol (mean QTc difference 10.73, 95% CI 7.09-14.37 milliseconds). Several top QT prolonging medications such as rifaximin, lactulose, cinacalcet, and lenalidomide were not previously known but have plausible mechanistic explanations. Significant interactions were observed between demographics or comorbidities and QTc prolongation with many medications, such as coronary disease and amiodarone. ConclusionsWe demonstrate a new, high-throughput technique for monitoring real-world effects of QTc-prolonging medications from readily accessible clinical data. Using this approach, we confirmed known medications for QTc prolongation and identified potential new associations and demographic or comorbidity interactions that could supplement findings in curated databases. Our single-center results would benefit from additional verification in future multisite studies that incorporate larger numbers of patients and ECGs along with more precise medication adherence and comorbidity data

High-Throughput Assessment of Real-World Medication Effects on QT Interval Prolongation: Observational Study

BackgroundDrug-induced prolongation of the corrected QT interval (QTc) increases the risk for Torsades de Pointes (TdP) and sudden cardiac death. Medication effects on the QTc have been studied in controlled settings but may not be well evaluated in real-world settings where medication effects may be modulated by patient demographics and comorbidities as well as the usage of other concomitant medications.ObjectiveWe demonstrate a new, high-throughput method leveraging electronic health records (EHRs) and the Surescripts pharmacy database to monitor real-world QTc-prolonging medication and potential interacting effects from demographics and comorbidities.MethodsWe included all outpatient electrocardiograms (ECGs) from September 2008 to December 2019 at a large academic medical system, which were in sinus rhythm with a heart rate of 40-100 beats per minute, QRS duration of &lt;120 milliseconds, and QTc of 300-700 milliseconds, determined using the Bazett formula. We used prescription information from the Surescripts pharmacy database and EHR medication lists to classify whether a patient was on a medication during an ECG. Negative control ECGs were obtained from patients not currently on the medication but who had been or would be on that medication within 1 year. We calculated the difference in mean QTc between ECGs of patients who are on and those who are off a medication and made comparisons to known medication TdP risks per the CredibleMeds.org database. Using linear regression analysis, we studied the interaction of patient-level demographics or comorbidities on medication-related QTc prolongation.ResultsWe analyzed the effects of 272 medications on 310,335 ECGs from 159,397 individuals. Medications associated with the greatest QTc prolongation were dofetilide (mean QTc difference 21.52, 95% CI 10.58-32.70 milliseconds), mexiletine (mean QTc difference 18.56, 95% CI 7.70-29.27 milliseconds), amiodarone (mean QTc difference 14.96, 95% CI 13.52-16.33 milliseconds), rifaximin (mean QTc difference 14.50, 95% CI 12.12-17.13 milliseconds), and sotalol (mean QTc difference 10.73, 95% CI 7.09-14.37 milliseconds). Several top QT prolonging medications such as rifaximin, lactulose, cinacalcet, and lenalidomide were not previously known but have plausible mechanistic explanations. Significant interactions were observed between demographics or comorbidities and QTc prolongation with many medications, such as coronary disease and amiodarone.ConclusionsWe demonstrate a new, high-throughput technique for monitoring real-world effects of QTc-prolonging medications from readily accessible clinical data. Using this approach, we confirmed known medications for QTc prolongation and identified potential new associations and demographic or comorbidity interactions that could supplement findings in curated databases. Our single-center results would benefit from additional verification in future multisite studies that incorporate larger numbers of patients and ECGs along with more precise medication adherence and comorbidity data

Practice Patterns and Patient Outcomes After Widespread Adoption of Remote Heart Failure Care

BackgroundAn unprecedented shift to remote heart failure outpatient care occurred during the coronavirus disease 2019 (COVID-19) pandemic. Given challenges inherent to remote care, we studied whether remote visits (video or telephone) were associated with different patient usage, clinician practice patterns, and outcomes.MethodsWe included all ambulatory cardiology visits for heart failure at a multisite health system from April 1, 2019, to December 31, 2019 (pre-COVID) or April 1, 2020, to December 31, 2020 (COVID era), resulting in 10 591 pre-COVID in-person, 7775 COVID-era in-person, 1009 COVID-era video, and 2322 COVID-era telephone visits. We used multivariable logistic and Cox proportional hazards regressions with propensity weighting and patient clustering to study ordering practices and outcomes.ResultsCompared with in-person visits, video visits were used more often by younger (mean 64.7 years [SD 14.5] versus 74.2 [14.1]), male (68.3% versus 61.4%), and privately insured (45.9% versus 28.9%) individuals (P&lt;0.05 for all). Remote visits were more frequently used by non-White patients (35.8% video, 37.0% telephone versus 33.2% in-person). During remote visits, clinicians were less likely to order diagnostic testing (odds ratio, 0.20 [0.18-0.22] video versus in-person, 0.18 [0.17-0.19] telephone versus in-person) or prescribe β-blockers (0.82 [0.68-0.99], 0.35 [0.26-0.47]), mineralocorticoid receptor antagonists (0.69 [0.50-0.96], 0.48 [0.35-0.66]), or loop diuretics (0.67 [0.53-0.85], 0.45 [0.37-0.55]). During telephone visits, clinicians were less likely to prescribe ACE (angiotensin-converting enzyme) inhibitor/ARB (angiotensin receptor blockers)/ARNIs (angiotensin receptor-neprilysin inhibitors; 0.54 [0.40-0.72]). Telephone visits but not video visits were associated with higher rates of 90-day mortality (1.82 [1.14-2.90]) and nonsignificant trends towards higher rates of 90-day heart failure emergency department visits (1.34 [0.97-1.86]) and hospitalizations (1.36 [0.98-1.89]).ConclusionsRemote visits for heart failure care were associated with reduced diagnostic testing and guideline-directed medical therapy prescription. Telephone but not video visits were associated with increased 90-day mortality