A critical appreciation of pathway analysis in atherosclerotic disease. Cellular phenotypic plasticity as an illustrative example

The rapid advancements in genome-scale (omics) techniques has created significant opportunities to investigate complex disease mechanisms in tissues and cells. Nevertheless, interpreting -omics data can be challenging, and pathway enrichment analysis is a frequently used method to identify candidate molecular pathways that drive gene expression changes. With a growing number of -omics studies dedicated to atherosclerosis, there has been a significant increase in studies and hypotheses relying on enrichment analysis. This brief review discusses the benefits and limitations of pathway enrichment analysis within atherosclerosis research. We highlight the challenges of identifying complex biological processes, such as cell phenotypic switching, within -omics data. Additionally, we emphasize the need for more comprehensive and curated gene sets that reflect the biological complexity of atherosclerosis. Pathway enrichment analysis is a valuable tool for gaining insights into the molecular mechanisms of atherosclerosis. Nevertheless, it is crucial to remain aware of the intrinsic limitations of this approach. By addressing these weaknesses, enrichment analysis in atherosclerosis can lead to breakthroughs in identifying the mechanisms of disease progresses, the identification of key driver genes, and consequently, advance personalized patient care

Addressing persistent evidence gaps in cardiovascular sex differences research – the potential of clinical care data

Women have historically been underrepresented in cardiovascular clinical trials, resulting in a lack of sex-specific data. This is especially problematic in two situations, namely those where diseases manifest differently in women and men and those where biological differences between the sexes might affect the efficacy and/or safety of medication. There is therefore a pressing need for datasets with proper representation of women to address questions related to these situations. Clinical care data could fit this bill nicely because of their unique broad scope across both patient groups and clinical measures. This perspective piece presents the potential of clinical care data in sex differences research and discusses current challenges clinical care data-based research faces. It also suggests strategies to reduce the effect of these limitations, and explores whether clinical care data alone will be sufficient to close evidence gaps or whether a more comprehensive approach is needed

Addressing persistent evidence gaps in cardiovascular sex differences research – the potential of clinical care data

Women have historically been underrepresented in cardiovascular clinical trials, resulting in a lack of sex-specific data. This is especially problematic in two situations, namely those where diseases manifest differently in women and men and those where biological differences between the sexes might affect the efficacy and/or safety of medication. There is therefore a pressing need for datasets with proper representation of women to address questions related to these situations. Clinical care data could fit this bill nicely because of their unique broad scope across both patient groups and clinical measures. This perspective piece presents the potential of clinical care data in sex differences research and discusses current challenges clinical care data-based research faces. It also suggests strategies to reduce the effect of these limitations, and explores whether clinical care data alone will be sufficient to close evidence gaps or whether a more comprehensive approach is needed

Heart Size Corrected Electrical Dyssynchrony and Its Impact on Sex-specific Response to Cardiac Resynchronization Therapy

Background - Women are less likely to receive cardiac resynchronization therapy (CRT), yet, they are more responsive to the therapy and respond at shorter QRS duration. The present study hypothesized that a relatively larger left ventricular (LV) electrical dyssynchrony in smaller hearts contributes to the better CRT response in women. For this the vectorcardiography-derived QRS area is used, since it allows for a more detailed quantification of electrical dyssynchrony compared to conventional electrocardiographic markers. Methods - Data from a multicenter registry of 725 CRT patients (median follow-up: 4.2 years [IQR: 2.7-6.1]) were analyzed. Baseline electrical dyssynchrony was evaluated using the QRS area, and the corrected QRS area for heart size using the LV end-diastolic volume (QRSarea/LVEDV). Impact of the QRSarea/LVEDV-ratio on the association between sex and LV reverse remodeling (end-systolic volume change: ΔLVESV) and sex and the composite outcome of all-cause mortality, LV assist device implantation or heart transplantation was assessed. Results - At baseline, women (n=228) displayed larger electrical dyssynchrony than men (QRS area: 132±55μVs vs 123±58μVs, p=0.043) which was, even more pronounced for the QRSarea/LVEDV-ratio (0.76±0.46μVs/ml vs 0.57±0.34μVs/ml, p<0.001). After multivariable analyses female sex was associated with ΔLVESV (β 0.12, p=0.003) and a lower occurrence the composite outcome (HR 0.59 (0.42-0.85), p=0.004). A part of the female advantage regarding reverse remodeling was attributed to the larger QRSarea/LVEDV-ratio in women (25-fold change in Beta from 0.12 to 0.09). The larger QRSarea/LVEDV-ratio did not contribute to the better survival observed in women. In both volumetric responders and non-responders, female sex remained strongly associated with a lower risk of the composite outcome (adjusted HR 0.59 (0.36-0.97), p=0.036 and 0.55 (0.33-0.90), p=0.018, respectively). Conclusions - Greater electrical dyssynchrony in smaller hearts contributes in part to more reverse remodeling observed in women after CRT, but this does not explain their better long-term outcomes

Increased circulating IgG levels, myocardial immune cells and IgG deposits support a role for an immune response in pre- and end-stage heart failure

The chronic inflammatory response plays an important role in adverse cardiac remodelling and the development of heart failure (HF). There is also evidence that in the pathogenesis of several cardiovascular diseases, chronic inflammation is accompanied by antibody and complement deposits in the heart, suggestive of a true autoimmune response. However, the role of antibody-mediated immune responses in HF progression is less clear. We assessed whether immune cell infiltration and immunoglobulin levels are associated with HF type and disease stage, taking sex differences into account. We found IgG deposits and increased infiltration of immune cells in the affected myocardium of patients with end-stage HF with reduced ejection fraction (HFrEF, n = 20). Circulating levels of IgG1 and IgG3 were elevated in these patients. Furthermore, the percentage of transitional/regulatory B cells was decreased (from 6.9% to 2.4%) compared with healthy controls (n = 5). Similarly, increased levels of circulating IgG1 and IgG3 were observed in men with left ventricular diastolic dysfunction (LVDD, n = 5), possibly an early stage of HF with preserved EF (HFpEF). In conclusion, IgG deposits and infiltrates of immune cells are present in end-stage HFrEF. In addition, both LVDD patients and end-stage HFrEF patients show elevated levels of circulating IgG1 and IgG3, suggesting an antibody-mediated immune response upon cardiac remodelling, which in the early phase of remodelling appear to differ between men and women. These immunoglobulin subclasses might be used as marker for pre-stage HF and its progression. Future identification of auto-antigens might open possibilities for new therapeutic interventions

Early electroencephalography for outcome prediction of postanoxic coma:A prospective cohort study

OBJECTIVE: To provide evidence that early electroencephalography (EEG) allows for reliable prediction of poor or good outcome after cardiac arrest.METHODS: In a 5-center prospective cohort study, we included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to 5 days. Five-minute EEG epochs were assessed by 2 reviewers, independently, at 8 predefined time points from 6 hours to 5 days after cardiac arrest, blinded for patients' actual condition, treatment, and outcome. EEG patterns were categorized as generalized suppression (&lt;10 μV), synchronous patterns with ≥50% suppression, continuous, or other. Outcome at 6 months was categorized as good (Cerebral Performance Category [CPC] = 1-2) or poor (CPC = 3-5).RESULTS: We included 850 patients, of whom 46% had a good outcome. Generalized suppression and synchronous patterns with ≥50% suppression predicted poor outcome without false positives at ≥6 hours after cardiac arrest. Their summed sensitivity was 0.47 (95% confidence interval [CI] = 0.42-0.51) at 12 hours and 0.30 (95% CI = 0.26-0.33) at 24 hours after cardiac arrest, with specificity of 1.00 (95% CI = 0.99-1.00) at both time points. At 36 hours or later, sensitivity for poor outcome was ≤0.22. Continuous EEG patterns at 12 hours predicted good outcome, with sensitivity of 0.50 (95% CI = 0.46-0.55) and specificity of 0.91 (95% CI = 0.88-0.93); at 24 hours or later, specificity for the prediction of good outcome was &lt;0.90.INTERPRETATION: EEG allows for reliable prediction of poor outcome after cardiac arrest, with maximum sensitivity in the first 24 hours. Continuous EEG patterns at 12 hours after cardiac arrest are associated with good recovery. ANN NEUROL 2019.</p

Sex Differences in Reported Adverse Drug Reactions to Angiotensin-Converting Enzyme Inhibitors

Sex differences in adverse drug reactions (ADRs) associated with angiotensin-converting enzyme inhibitors (ACEIs) remain poorly understood owing to a lack of sex-specific ADR data from clinical trials. 1 Postmarketing pharmacovigilance data, containing structured and detailed ADR information, may play an important role in such analyses. However, these data are often not corrected for prescription numbers and therefore cannot separate sex differences in ADR risk from sex differences in prescription rates. To investigate whether women report more ACEI-related ADRs than men after correction for sex-specific prescription and describe sex differences in reported ADR types, we combined data from the global pharmacovigilance database VigiBase and the prescription-corrected Dutch pharmacovigilance database Lareb

Correction to: Evaluation of non-invasive imaging parameters in coronary microvascular disease: a systematic review

Following the publication of the original article [1] the authors became aware of an error in Fig. 2. Unfortunately, the Figure showed all included studies instead of only the studies with the specific measurement mentioned in the Figure caption. The studies that showed a different measure of coronary microvascular dysfunction should have been removed. The rectified Figure is shown here below, as well as the original article, which has now been updated

Sex Differences in Reported Adverse Drug Reactions to Angiotensin-Converting Enzyme Inhibitors

This cross-sectional study investigates differences by sex in reporting of adverse drug reactions associated with angiotensin-converting enzyme inhibitors combining global and prescription-corrected databases

Outcome Prediction of Postanoxic Coma:A Comparison of Automated Electroencephalography Analysis Methods

BACKGROUND: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. METHODS: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. RESULTS: The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44-64%) at a false positive rate (FPR) of 0% (95% CI 0-2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52-100%) at a FPR of 12% (95% CI 0-24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83-83%) at a FPR of 3% (95% CI 3-3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. CONCLUSIONS: A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest