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Prospective longitudinal follow‐up of left ventricular ejection fraction (LVEF) trajectories after acute cardiac decompensation of heart failure is lacking. We investigated changes in LVEF and covariates at 6‐months' follow‐up in patients with a predischarge LVEF ≤40%, and determined predictors and prognostic implications of LVEF changes through 18‐months' follow‐up.
Methods and Results
Interdisciplinary Network Heart Failure program participants (n=633) were categorized into subgroups based on LVEF at 6‐months' follow‐up: normalized LVEF (>50%; heart failure with normalized ejection fraction, n=147); midrange LVEF (41%–50%; heart failure with midrange ejection fraction, n=195), or persistently reduced LVEF (≤40%; heart failure with persistently reduced LVEF , n=291). All received guideline‐directed medical therapies. At 6‐months' follow‐up, compared with patients with heart failure with persistently reduced LVEF, heart failure with normalized LVEF or heart failure with midrange LVEF subgroups showed greater reductions in LV end‐diastolic/end‐systolic diameters (both P<0.001), and left atrial systolic diameter (P=0.002), more increased septal/posterior end‐diastolic wall‐thickness (both P<0.001), and significantly greater improvement in diastolic function, biomarkers, symptoms, and health status. Heart failure duration <1 year, female sex, higher predischarge blood pressure, and baseline LVEF were independent predictors of LVEF improvement. Mortality and event‐free survival rates were lower in patients with heart failure with normalized LVEF (P=0.002). Overall, LVEF increased further at 18‐months' follow‐up (P<0.001), while LV end‐diastolic diameter decreased (P=0.048). However, LVEF worsened (P=0.002) and LV end‐diastolic diameter increased (P=0.047) in patients with heart failure with normalized LVEF hospitalized between 6‐months' follow‐up and 18‐months' follow‐up.
Conclusions
Six‐month survivors of acute cardiac decompensation for systolic heart failure showed variable LVEF trajectories, with >50% showing improvements by ≥1 LVEF category. LVEF changes correlated with various parameters, suggesting multilevel reverse remodeling, were predictable from several baseline characteristics, and were associated with clinical outcomes at 18‐months' follow‐up. Repeat hospitalizations were associated with attenuation of reverse remodeling."
Since the addition of fluoride to drinking water in the 1940s, there have been frequent and sometimes heated discussions regarding its benefits and risks. In a recently published review, we addressed the question if current exposure levels in Europe represent a risk to human health. This review was discussed in an editorial asking why we did not calculate benchmark doses (BMD) of fluoride neurotoxicity for humans. Here, we address the question, why it is problematic to calculate BMDs based on the currently available data. Briefly, the conclusions of the available studies are not homogeneous, reporting negative as well as positive results; moreover, the positive studies lack control of confounding factors such as the influence of well-known neurotoxicants. We also discuss the limitations of several further epidemiological studies that did not meet the inclusion criteria of our review. Finally, it is important to not only focus on epidemiological studies. Rather, risk analysis should consider all available data, including epidemiological, animal, as well as in vitro studies. Despite remaining uncertainties, the totality of evidence does not support the notion that fluoride should be considered a human developmental neurotoxicant at current exposure levels in European countries.
Aims
Ischaemic stroke (IS) might induce alterations of cardiac function. Prospective data on frequency of cardiac dysfunction and heart failure (HF) after IS are lacking. We assessed prevalence and determinants of diastolic dysfunction (DD), systolic dysfunction (SD), and HF in patients with acute IS.
Methods and results
The Stroke‐Induced Cardiac FAILure in mice and men (SICFAIL) study is a prospective, hospital‐based cohort study. Patients with IS underwent a comprehensive assessment of cardiac function in the acute phase (median 4 days after IS) including clinical examination, standardized transthoracic echocardiography by expert sonographers, and determination of blood‐based biomarkers. Information on demographics, lifestyle, risk factors, symptoms suggestive of HF, and medical history was collected by a standardized personal interview. Applying current guidelines, cardiac dysfunction was classified based on echocardiographic criteria into SD (left ventricular ejection fraction < 52% in men or <54% in women) and DD (≥3 signs of DD in patients without SD). Clinically overt HF was classified into HF with reduced, mid‐range, or preserved ejection fraction. Between January 2014 and February 2017, 696 IS patients were enrolled. Of them, patients with sufficient echocardiographic data on SD were included in the analyses {n = 644 patients [median age 71 years (interquartile range 60–78), 61.5% male]}. In these patients, full assessment of DD was feasible in 549 patients without SD (94%). Prevalence of cardiac dysfunction and HF was as follows: SD 9.6% [95% confidence interval (CI) 7.6–12.2%]; DD in patients without SD 23.3% (95% CI 20.0–27.0%); and clinically overt HF 5.4% (95% CI 3.9–7.5%) with subcategories of HF with preserved ejection fraction 4.35%, HF with mid‐range ejection fraction 0.31%, and HF with reduced ejection fraction 0.78%. In multivariable analysis, SD and fulfilment of HF criteria were associated with history of coronary heart disease [SD: odds ratio (OR) 3.87, 95% CI 1.93–7.75, P = 0.0001; HF: OR 2.29, 95% CI 1.04–5.05, P = 0.0406] and high‐sensitive troponin T at baseline (SD: OR 1.78, 95% CI 1.31–2.42, P = 0.0003; HF: OR 1.66, 95% CI 1.17–2.33, P = 0.004); DD was associated with older age (OR 1.08, 95% CI 1.05–1.11, P < 0.0001) and treated hypertension vs. no hypertension (OR 2.84, 95% CI 1.23–6.54, P = 0.0405).
Conclusions
A substantial proportion of the study population exhibited subclinical and clinical cardiac dysfunction. SICFAIL provides reliable data on prevalence and determinants of SD, DD, and clinically overt HF in patients with acute IS according to current guidelines, enabling further clarification of its aetiological and prognostic role.
Background:
Evidence that home telemonitoring for patients with chronic heart failure (CHF) offers clinical benefit over usual care is controversial as is evidence of a health economic advantage.
Methods:
Between January 2010 and June 2013, patients with a confirmed diagnosis of CHF were enrolled and randomly assigned to 2 study groups comprising usual care with and without an interactive bi-directional remote monitoring system (Motiva\(^{®}\)). The primary endpoint in CardioBBEAT is the Incremental Cost-Effectiveness Ratio (ICER) established by the groups' difference in total cost and in the combined clinical endpoint "days alive and not in hospital nor inpatient care per potential days in study" within the follow-up of 12 months.
Results:
A total of 621 predominantly male patients were enrolled, whereof 302 patients were assigned to the intervention group and 319 to the control group. Ischemic cardiomyopathy was the leading cause of heart failure. Despite randomization, subjects of the control group were more often in NYHA functional class III-IV, and exhibited peripheral edema and renal dysfunction more often. Additionally, the control and intervention groups differed in heart rhythm disorders. No differences existed regarding risk factor profile, comorbidities, echocardiographic parameters, especially left ventricular and diastolic diameter and ejection fraction, as well as functional test results, medication and quality of life. While the observed baseline differences may well be a play of chance, they are of clinical relevance. Therefore, the statistical analysis plan was extended to include adjusted analyses with respect to the baseline imbalances.
Conclusions:
CardioBBEAT provides prospective outcome data on both, clinical and health economic impact of home telemonitoring in CHF. The study differs by the use of a high evidence level randomized controlled trial (RCT) design along with actual cost data obtained from health insurance companies. Its results are conducive to informed political and economic decision-making with regard to home telemonitoring solutions as an option for health care. Overall, it contributes to developing advanced health economic evaluation instruments to be deployed within the specific context of the German Health Care System.
The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.
Biological systems such as cells or whole organisms are governed by complex regulatory networks of transcription factors, hormones and other regulators which determine the behavior of the system depending on internal and external stimuli. In mathematical models of these networks, genes are represented by interacting “nodes” whose “value” represents the activity of the gene.
Control processes in these regulatory networks are challenging to elucidate and quantify. Previous control centrality metrics, which aim to mathematically capture the ability of individual nodes to control biological systems, have been found to suffer from problems regarding biological plausibility.
This thesis presents a new approach to control centrality in biological networks. Three types of network control are distinguished: Total control centrality quantifies the impact of gene mutations and identifies potential pharmacological targets such as genes involved in oncogenesis (e.g. zinc finger protein GLI2 or bone morphogenetic proteins in chondrocytes). Dynamic control centrality describes relaying functions as observed in signaling cascades (e.g control in mouse colon stem cells). Value control centrality measures the direct influence of the value of the node on the network (e.g. Indian hedgehog as an essential regulator of proliferation in chondrocytes). Well-defined network manipulations define all three centralities not only for nodes, but also for the interactions between them, enabling detailed insights into network pathways.
The calculation of the new metrics is made possible by substantial computational improvements in the simulation algorithms for several widely used mathematical modeling paradigms for genetic regulatory networks, which are implemented in the regulatory network simulation framework Jimena created for this thesis.
Applying the new metrics to biological networks and artificial random networks shows how these mathematical concepts correspond to experimentally verified gene functions and signaling pathways in immunity and cell differentiation. In contrast to controversial previous results even from the Barabási group, all results indicate that the ability to control biological networks resides in only few driver nodes characterized by a high number of connections to the rest of the network. Autoregulatory loops strongly increase the controllability of the network, i.e. its ability to control itself, and biological networks are characterized by high controllability in conjunction with high robustness against mutations, a combination that can be achieved best in sparsely connected networks with densities (i.e. connections to nodes ratios) around 2.0 - 3.0.
The new concepts are thus considerably narrowing the gap between network science and biology and can be used in various areas such as system modeling, plausibility trials and system analyses.
Medical applications discussed in this thesis include the search for oncogenes and pharmacological targets, as well their functional characterization.
Background: Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed.
Results: (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones).
Conclusions: Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.
Control of genetic regulatory networks is challenging to define and quantify. Previous control centrality metrics, which aim to capture the ability of individual nodes to control the system, have been found to suffer from plausibility and applicability problems. Here we present a new approach to control centrality based on network convergence behaviour, implemented as an extension of our genetic regulatory network simulation framework Jimena (http://stefan-karl.de/jimena). We distinguish three types of network control, and show how these mathematical concepts correspond to experimentally verified node functions and signalling pathways in immunity and cell differentiation: Total control centrality quantifies the impact of node mutations and identifies potential pharmacological targets such as genes involved in oncogenesis (e.g. zinc finger protein GLI2 or bone morphogenetic proteins in chondrocytes). Dynamic control centrality describes relaying functions as observed in signalling cascades (e.g. src kinase or Jak/Stat pathways). Value control centrality measures the direct influence of the value of the node on the network (e.g. Indian hedgehog as an essential regulator of proliferation in chondrocytes). Surveying random scale-free networks and biological networks, we find that control of the network resides in few high degree driver nodes and networks can be controlled best if they are sparsely connected.
Health-related quality of life (HRQL) among migrant populations can be associated with acculturation (i.e., the process of adopting, acquiring and adjusting to a new cultural environment). Since there is a lack of longitudinal studies, we aimed to describe HRQL changes among adults of Turkish descent living in Berlin and Essen, Germany, and their association with acculturation. Participants of a population-based study were recruited in 2012–2013 and reinvited six years later to complete a questionnaire. Acculturation was assessed at baseline using the Frankfurt acculturation scale (integration, assimilation, separation and marginalization). HRQL was assessed at baseline (SF-8) and at follow-up (SF-12) resulting in a physical (PCS) and mental (MCS) sum score. Associations with acculturation and HRQL were analyzed with linear regression models using a time-by-acculturation status interaction term. In the study 330 persons were included (65% women, mean age ± standard deviation 43.3 ± 11.8 years). Over the 6 years, MCS decreased, while PCS remained stable. While cross-sectional analyses showed associations of acculturation status with both MCS and PCS, temporal changes including the time interaction term did not reveal associations of baseline acculturation status with HRQL. When investigating HRQL in acculturation, more longitudinal studies are needed to take changes in both HRQL and acculturation status into account.
Altered autophagy accompanied by abnormal autophagic (rimmed) vacuoles detectable by light and electron microscopy is a common denominator of many familial and sporadic non‐inflammatory muscle diseases. Even in the era of next generation sequencing (NGS), late‐onset vacuolar myopathies remain a diagnostic challenge. We identified 32 adult vacuolar myopathy patients from 30 unrelated families, studied their clinical, histopathological and ultrastructural characteristics and performed genetic testing in index patients and relatives using Sanger sequencing and NGS including whole exome sequencing (WES). We established a molecular genetic diagnosis in 17 patients. Pathogenic mutations were found in genes typically linked to vacuolar myopathy (GNE, LDB3/ZASP, MYOT, DES and GAA), but also in genes not regularly associated with severely altered autophagy (FKRP, DYSF, CAV3, COL6A2, GYG1 and TRIM32) and in the digenic facioscapulohumeral muscular dystrophy 2. Characteristic histopathological features including distinct patterns of myofibrillar disarray and evidence of exocytosis proved to be helpful to distinguish causes of vacuolar myopathies. Biopsy validated the pathogenicity of the novel mutations p.(Phe55*) and p.(Arg216*) in GYG1 and of the p.(Leu156Pro) TRIM32 mutation combined with compound heterozygous deletion of exon 2 of TRIM32 and expanded the phenotype of Ala93Thr‐caveolinopathy and of limb‐girdle muscular dystrophy 2i caused by FKRP mutation. In 15 patients no causal variants were detected by Sanger sequencing and NGS panel analysis. In 12 of these cases, WES was performed, but did not yield any definite mutation or likely candidate gene. In one of these patients with a family history of muscle weakness, the vacuolar myopathy was eventually linked to chloroquine therapy. Our study illustrates the wide phenotypic and genotypic heterogeneity of vacuolar myopathies and validates the role of histopathology in assessing the pathogenicity of novel mutations detected by NGS. In a sizable portion of vacuolar myopathy cases, it remains to be shown whether the cause is hereditary or degenerative.