Citations

Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality worldwide, hence significant efforts have been made to establish behavior and risk factors associated with CVD. The American Heart Association proposed a 7-metric tool to promote ideal cardiovascular health (CVH). Recent data demonstrated that a higher number of ideal CVH metrics was associated with a lower risk of CVD, stroke, and mortality. Our study aimed to perform a systematic review and meta-analysis of prospective studies investigating the association of ideal CVH metrics and CVD, stroke, and cardiovascular mortality (CVM) in the general population. Medline and Scopus databases were searched from January 2010 to June 2022 for prospective studies reporting CVH metrics and outcomes on composite-CVD, coronary heart disease, myocardial infarction, stroke, and CVM. Each CVH metrics group was compared to another. Twenty-two studies totaling 3,240,660 adults (57.8% men) were analyzed. The follow-up duration was 12.0 ± 7.2 years. Our analysis confirmed that a higher number of ideal CVH metrics led to lower risk for CVD and CVM (statistically significant for composite-CVD, stroke, and CVM; p < 0.05). Conclusion: Even modest improvements in CVH are associated with CV-morbidity and mortality benefits, providing a strong public health message about the importance of a healthier lifestyle.

Despite the high incidence and burden of stroke, biological biomarkers are not used routinely in clinical practice to diagnose, determine progression, or prognosticate outcomes of acute ischemic stroke (AIS). Because of its direct interface with neural tissue, cerebrospinal fluid (CSF) is a potentially valuable source for biomarker development. This systematic review was conducted using three databases. All trials investigating clinical and preclinical models for CSF biomarkers for AIS diagnosis, prognostication, and severity grading were included, yielding 22 human trials and five animal studies for analysis. In total, 21 biomarkers and other multiomic proteomic markers were identified. S100B, inflammatory markers (including tumor necrosis factor-alpha and interleukin 6), and free fatty acids were the most frequently studied biomarkers. The review showed that CSF is an effective medium for biomarker acquisition for AIS. Although CSF is not routinely clinically obtained, a potential benefit of CSF studies is identifying valuable biomarkers from the pathophysiologic microenvironment that ultimately inform optimization of targeted low-abundance assays from peripheral biofluid samples (e.g., plasma). Several important catabolic and anabolic markers can serve as effective measures of diagnosis, etiology identification, prognostication, and severity grading. Trials with large cohorts studying the efficacy of biomarkers in altering clinical management are still needed.

Abstract Amyloid light-chain (AL) amyloidosis is a rare, typically fatal disease characterized by the accumulation of misfolded immunoglobulin light chains (LCs). Birtamimab is an investigational humanized monoclonal antibody designed to neutralize toxic LC aggregates and deplete insoluble organ-deposited amyloid via macrophage-induced phagocytosis. VITAL was a phase 3 randomized, double-blind, placebo-controlled clinical trial assessing the efficacy and safety of birtamimab + standard of care (SOC) in 260 newly diagnosed, treatment-naive patients with AL amyloidosis. Patients received 24 mg/kg IV birtamimab + SOC or placebo + SOC every 28 days. The primary composite end point was the time to all-cause mortality (ACM) or centrally adjudicated cardiac hospitalization ≥91 days after the first study drug infusion. The trial was terminated early after an interim futility analysis; there was no significant difference in the primary composite end point (hazard ratio [HR], 0.826; 95% confidence interval [CI], 0.574-1.189; log-rank P = .303). A post hoc analysis of patients with Mayo stage IV AL amyloidosis, those at the highest risk of early mortality, showed significant improvement in the time to ACM with birtamimab at month 9 (HR, 0.413; 95% CI, 0.191-0.895; log-rank P = .021). At month 9, 74% of patients with Mayo stage IV AL amyloidosis treated with birtamimab and 49% of those given placebo survived. Overall, the rates of treatment-emergent adverse events (TEAEs) and serious TEAEs were generally similar between treatment arms. A confirmatory phase 3 randomized, double-blind, placebo-controlled clinical trial of birtamimab in patients with Mayo stage IV AL amyloidosis (AFFIRM-AL; NCT04973137) is currently enrolling. The VITAL trial was registered at www.clinicaltrials.gov as #NCT02312206.

Abstract The link between bipolar disorder (BP) and immune dysfunction remains controversial. While epidemiological studies have long suggested an association, recent research has found only limited evidence of such a relationship. To clarify this, we investigated the contributions of immune-relevant genetic factors to the response to lithium (Li) treatment and the clinical presentation of BP. First, we assessed the association of a large collection of immune-related genes (4,925) with Li response, defined by the Retrospective Assessment of the Lithium Response Phenotype Scale (Alda scale), and clinical characteristics in patients with BP from the International Consortium on Lithium Genetics (ConLi+Gen, N = 2,374). Second, we calculated here previously published polygenic scores (PGSs) for immune-related traits and evaluated their associations with Li response and clinical features. We found several genes associated with Li response at p < 1x10− 4 values, including HAS3, CNTNAP5 and NFIB. Network and functional enrichment analyses uncovered an overrepresentation of pathways involved in cell adhesion and intercellular communication, which appear to converge on the well-known Li-induced inhibition of GSK-3β. We also found various genes associated with BP’s age-at-onset, number of mood episodes, and presence of psychosis, substance abuse and/or suicidal ideation at the exploratory threshold. These included RTN4, XKR4, NRXN1, NRG1/3 and GRK5. Additionally, PGS analyses suggested serum FAS, ECP, TRANCE and cytokine ligands, amongst others, might represent potential circulating biomarkers of Li response and clinical presentation. Taken together, our results support the notion of a relatively weak association between immunity and clinically relevant features of BP at the genetic level.

Abstract Purpose: Endometrioid ovarian carcinoma (ENOC) is the second most-common type of ovarian carcinoma, comprising 10%–20% of cases. Recently, the study of ENOC has benefitted from comparisons to endometrial carcinomas including defining ENOC with four prognostic molecular subtypes. Each subtype suggests differential mechanisms of progression, although tumor-initiating events remain elusive. There is evidence that the ovarian microenvironment may be critical to early lesion establishment and progression. However, while immune infiltrates have been well studied in high-grade serous ovarian carcinoma, studies in ENOC are limited. Experimental Design: We report on 210 ENOC, with clinical follow-up and molecular subtype annotation. Using multiplex IHC and immunofluorescence, we examine the prevalence of T-cell lineage, B-cell lineage, macrophages, and populations with programmed cell death protein 1 or programmed death-ligand 1 across subtypes of ENOC. Results: Immune cell infiltrates in tumor epithelium and stroma showed higher densities in ENOC subtypes with known high mutation burden (POLEmut and MMRd). While molecular subtypes were prognostically significant, immune infiltrates were not (overall survival P &gt; 0.2). Analysis by molecular subtype revealed that immune cell density was prognostically significant in only the no specific molecular profile (NSMP) subtype, where immune infiltrates lacking B cells (TILB minus) had inferior outcome (disease-specific survival: HR, 4.0; 95% confidence interval, 1.1–14.7; P &lt; 0.05). Similar to endometrial carcinomas, molecular subtype stratification was generally superior to immune response in predicting outcomes. Conclusions: Subtype stratification is critical for better understanding of ENOC, in particular the distribution and prognostic significance of immune cell infiltrates. The role of B cells in the immune response within NSMP tumors warrants further study.

Antiplatelet therapy is the mainstay of pharmacologic treatment to prevent thrombotic or ischemic events in patients with coronary artery disease treated with percutaneous coronary intervention and those treated medically for an acute coronary syndrome. The use of antiplatelet therapy comes at the expense of an increased risk of bleeding complications. Defining the optimal intensity of platelet inhibition according to the clinical presentation of atherosclerotic cardiovascular disease and individual patient factors is a clinical challenge. Modulation of antiplatelet therapy is a medical action that is frequently performed to balance the risk of thrombotic or ischemic events and the risk of bleeding. This aim may be achieved by reducing (ie, de-escalation) or increasing (ie, escalation) the intensity of platelet inhibition by changing the type, dose, or number of antiplatelet drugs. Because de-escalation or escalation can be achieved in different ways, with a number of emerging approaches, confusion arises with terminologies that are often used interchangeably. To address this issue, this Academic Research Consortium collaboration provides an overview and definitions of different strategies of antiplatelet therapy modulation for patients with coronary artery disease, including but not limited to those undergoing percutaneous coronary intervention, and consensus statements on standardized definitions.

Abstract Background and Aims Acute kidney injury (AKI) is associated with an increased risk of progression to a severe form in every disease and, moreover, is associated with an increased mortality, and so it is also with coronavirus disease 2019 (COVID-19). AKI is a frequent complication in COVID-19 patients admitted to ICU for severe respiratory failure. ICU treatment itself indirectly could cause or exacerbate renal damage, through suboptimal fluid management, drug toxicity, or low human resources. The aims of this study were to assess AKI prevalence, fluid balance and glomerular filtration rate as a predictor of outcome in COVID-19 patients treated in the intensive care unit. Method We retrospectively analysed all adult patients admitted to the ICU at a regional hospital in northern Bavaria, from 1st March to 31st December 2020. Clinical data and laboratory results were retrospectively retrieved from the hospital information system and electronic case files. According to the severity of AKI based on the KDIGO criteria, and laboratory values were followed on the first 7 days on the ICU. In total 320 patients patients were included in the study. Level of significance was set to p&lt;0.05. Results The mean age was 65 ± 19 years, 135 (42%) patients were females, 185 (58%) were males. Median length of ICU stay was 7 (2-13 days) in females, and 8 days (4- to 21 days) in males. At the ICU, 177 patients needed some form of ventilation, from which 109 (needed Invasive mechanical ventilation). During ICU stay, 81 patients have died, with fatality rate of 25%. Patients with AKI (n=48, 15%) at the admission to the ICU, had 2.1 times higher risk of death at the ICU than patients without AKI. There was a statistically significant difference in ICU survival based on a glomerular filtration rate, fluid balance, CRP and leukocyte values, F=1.957, p=0.026; Wilk's Λ=0.724, partial η2 = 0.276 Conclusion Acute kidney injury is common in coronavirus disease 2019, and it is associated with poor clinical outcomes. Even first couple of days at the ICU could point to the outcome, with markers of inflammation, glomerular filtration rate, and fluid balance being most valuable variables.

Abstract Medulloblastoma (MB) is a highly malignant childhood brain tumor comprising a collection of molecularly and clinically distinct entities. Mutations in chromatin-modifying genes distribute across subgroups and account for nearly half of all MBs. However, the molecular consequences of chromatin modifier alterations remain poorly understood. Herein, we characterize the chromatin landscape of fifty-two primary MBs representing each MB subgroup using chromatin immunoprecipitation followed by sequencing (ChIP-seq) for six histone marks and perform multi-layered integration with somatic genetics, genome-wide DNA methylation, and transcriptomic datasets extracted from the same tumors. Analysis of differential chromatin states across MB subgroups identified significant enrichment of a bivalent enhancer state (EnhBiv) in Group 4 MB. Group 4-specific EnhBivs mapped to promoters of key neurodevelopmental genes and transcription factors and was associated with increased DNA methylation and reduced expression. In accordance with higher EnhBivs coverage in Group 4 MB, we found that KDM2B, a H3K4me3 and H3K36me2 demethylase, is highly expressed in Group 4 MBs and patient-derived xenografts (PDXs). KDM2B knockout mediated by CRISPR targeting suppressed growth of both Group 3 MB cell lines in vitro and Group 4 MB PDXs in vivo but had no effect on control SHH-MB cells, suggesting that KDM2B is a novel selective dependency in MB. Knockout of KDM2B in MB cells induced profound upregulation of PRC2 targets, genes regulated by bivalent histone marks, as well as genes involved in neuronal differentiation. Further biochemical and molecular studies demonstrated that KDM2B interacts with PRC2 complex subunits and occupies gene promoters associated with Group 4-specific EnhBiv chromatin. In summary, our comprehensive characterization of the MB chromatin landscape in a large cohort of primary tumors provides novel insights into the epigenetic basis of MB and implicates KDM2B as a novel Group 4 MB dependency that should be prioritized as a therapeutic target in this subgroup.

Abstract Aims Left ventricular hypertrophy (LVH) is an established, independent predictor of cardiovascular disease. Indices derived from the electrocardiogram (ECG) have been used to infer the presence of LVH with limited sensitivity. This study aimed to classify LVH defined by cardiovascular magnetic resonance (CMR) imaging using the 12-lead ECG for cost-effective patient stratification. Methods and results We extracted ECG biomarkers with a known physiological association with LVH from the 12-lead ECG of 37 534 participants in the UK Biobank imaging study. Classification models integrating ECG biomarkers and clinical variables were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% training and 20% test sets for performance evaluation. Ten-fold cross validation was applied with further validation testing performed by separating data based on UK Biobank imaging centres. QRS amplitude and blood pressure (P &lt; 0.001) were the features most strongly associated with LVH. Classification with logistic regression had an accuracy of 81% [sensitivity 70%, specificity 81%, Area under the receiver operator curve (AUC) 0.86], SVM 81% accuracy (sensitivity 72%, specificity 81%, AUC 0.85) and RF 72% accuracy (sensitivity 74%, specificity 72%, AUC 0.83). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical variables alone. Validation testing by UK Biobank imaging centres demonstrated robustness of our models. Conclusion A combination of ECG biomarkers and clinical variables were able to predict LVH defined by CMR. Our findings provide support for the ECG as an inexpensive screening tool to risk stratify patients with LVH as a prelude to advanced imaging.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.