January 2020 at a Glance: Translational Medicine, Predictors of Outcome and Treatments

Right ventricular dysfunction is a major determinant of the poor prognosis of heart failure (HF) patients.1 Lim and Gustafsson2 summarized physiological interpretation and evidence on pulmonary artery pulsatility index, a parameter assessing right heart function, in patients with advanced HF or cardiogenic shock. Target therapy for transthyretin amyloidosis is one of the main recent achievements in HF treatment.3 Müller et al.4 reviewed evidence about new specific drugs targeting transthyretin amyloidosis and potential future implications. Berulava et al.5 found that about one quarter of the transcripts of healthy mouse and human heart exhibit m6-adenosine methylation (m6A) of RNA. Changes in m6A RNA methylation were related with progression to HF with hypermethylated transcripts mainly linked to processes that control the response to muscle stretch, growth factors and heart morphogenesis. Mice with a cardiomyocyte restricted knockout of the RNA demethylase exhibited impaired cardiac function compared to control mice. Thus, m6A RNA methylation is a new transcription-independent mechanism of translation, is related to HF progression and may be a therapeutic target. Cao et al.6 compared the plasma proteome of HF patients with or without clinical events. They found changes in the proteins related to the glutathione, arginine and proline, and pyruvate metabolism in the patients who died or were rehospitalized, compared with those with a stable clinical course. Ferreira et al.7 used biomarker analysis to compare mechanisms related to cardiovascular (CV) and non-CV death in 2309 patients with HF from the BIOSTAT-CHF (a systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Troponin predicted CV death, while N-terminal pro-B-type natriuretic peptide levels were associated with both CV and non-CV death. Ageing is an established major determinant of adequacy and adherence to treatment and of survival in patients with HF.8-11 Lainščak et al.12 evaluated age- and sex-related differences in HF management, mortality and hospitalization in the patients enrolled in the ESC HFA EORP HF Long-Term registry. Age >75 years was associated with underutilization of optimal medical therapy and with increased mortality. Different from another recent analysis, showing better survival in women with dilated cardiomyopathy,13 sex was not an independent predictor of increased mortality. Beta-blockers are underused in elderly subjects.8 Stolfo et al.14 investigated the association between beta-blocker use and clinical events in 6562 elderly patients (≥80 years) with HF and reduced ejection fraction in the Swedish HF Registry. Beta-blocker use was associated with a reduced risk of all-cause mortality [hazard ratio (HR) 0.89, 95% confidence interval (CI) 0.79–0.99] and of CV events also in these patients. Type 2 diabetes, or obesity, atrial fibrillation (AF) and HF with preserved ejection fraction (HFpEF) often coexist.15 Studying a cohort of patients with AF, Polovina et al.16 showed that patients with type 2 diabetes had a 85% greater risk of HF events (adjusted HR 1.85, 95% CI 1.51–2.28), including a 45% increased risk for new-onset HF (adjusted HR 1.45, 95% CI 1.17–2.28) and greater risk of all-cause and CV mortality. Among new-onset HF phenotypes, 67% had HFpEF with an adjusted HR of 2.38 (95% CI 1.30–4.58) in patients with diabetes. Consistently, an analysis of patients with AF enrolled in EMPA-REG-OUTCOME showed an increased risk of events in those with AF at baseline. Empagliflozin, compared to placebo, reduced CV death or HF hospitalization consistently also in patients with AF (HR 0.58, 95% CI 0.36–0.92) with similar results for the components of this endpoint, all-cause mortality, new or worsening nephropathy, first introduction of loop diuretics, or occurrence of oedema.17 Machine learning may be used to identify patients responding to HF therapies such as cardiac resynchronization.18 Adler et al.19 used a machine-learning algorithm based on eight easy variables to predict mortality in HF patients. Also Segar et al.20 used machine-learning analysis to identify three phenotypes of HFpEF with different clinical characteristics and outcomes. Both algorithms were externally validated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.