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Background Socioeconomic deprivation is associated with adverse clinical outcomes in patients with heart failure (HF). However, in the context of improved medical and device therapy for HF, it is unknown whether the influence of socioeconomic deprivation on HF outcomes is changing over time, especially in relation to evolving life expectancy patterns in the general population. Therefore, we aimed to describe temporal trends in the association of socioeconomic deprivation with loss of actuarially predicted life expectancy amongst ambulatory patients with HF. Methods Between 2006 and 2014, 1802 patients (73.2% male, mean age 69.6 years) with HF and left ventricular ejection fraction ≤ 45% were consecutively recruited across four hospitals in the United Kingdom (UK). Patients were stratified into socioeconomic deprivation tertiles defined by the UK Index of Multiple Deprivation (IMD) score with IMD tertile 1 denoting the least deprived and IMD tertile 3 the most deprived. The primary outcome was all-cause mortality, and relative survival predictions—in relation to age- and sex-matched background mortality rates—were calculated using UK National Life Tables. Relative survival was illustrated in terms of excess mortality risk and years of life expectancy lost. Recruitment period was split into 3-year intervals (2006–2008, 2009–2011 and 2012–2014). Results During a median follow-up of 5.0 years, 1302 participants (72.3%) died. Unadjusted mortality rate was highest in tertile 2. However, adjusted to the age–sex matched UK population, a stepwise increase in excess mortality risk was observed across tertiles, with tertile 1 experiencing an excess mortality risk of 11.1% (95% CI: 6.1–16.1%) and tertile 3 24.2% (95% CI: 19.4–28.0%). This corresponded to a loss of life expectancy of 1.76 years (95% CI: 1.50–2.03) for tertile 1 and 2.30 years (95% CI: 2.03–2.57) for tertile 3 over a 10-year period. We observed disparity in actuarial survival between tertiles over time, with participants in tertile 1 losing less life expectancy at 10 years compared to those in tertiles 2 and 3. However this was only statistically significant for those recruited between 2012 and 2014 ( p  < 0.05). Conclusions The impact of socioeconomic deprivation on HF outcomes in an unselected diverse UK population appears to have worsened over time.

Brown and beige adipose tissue are emerging as distinct endocrine organs. These tissues are functionally associated with skeletal muscle, adipose tissue metabolism and systemic energy expenditure, suggesting an interorgan signaling network. Using metabolomics, we identify 3-methyl-2-oxovaleric acid, 5-oxoproline, and β-hydroxyisobutyric acid as small molecule metabokines synthesized in browning adipocytes and secreted via monocarboxylate transporters. 3-methyl-2-oxovaleric acid, 5-oxoproline and β-hydroxyisobutyric acid induce a brown adipocyte-specific phenotype in white adipocytes and mitochondrial oxidative energy metabolism in skeletal myocytes both in vitro and in vivo. 3-methyl-2-oxovaleric acid and 5-oxoproline signal through cAMP-PKA-p38 MAPK and β-hydroxyisobutyric acid via mTOR. In humans, plasma and adipose tissue 3-methyl-2-oxovaleric acid, 5-oxoproline and β-hydroxyisobutyric acid concentrations correlate with markers of adipose browning and inversely associate with body mass index. These metabolites reduce adiposity, increase energy expenditure and improve glucose and insulin homeostasis in mouse models of obesity and diabetes. Our findings identify beige adipose-brown adipose-muscle physiological metabokine crosstalk. Beige and brown fat may influence systemic metabolism through secreted signals. Here the authors identify a panel of metabolites secreted from beige and brown fat cells, which signal to influence fat tissue and skeletal muscle metabolism and have anti-obesity effects in mouse models of obesity and diabetes.

Generator longevity is the key issue for patients, and is also important for payers, yet implanters of Cardiac Implantable Electronic Devices (CIEDs) face a challenge when selecting the appropriate device since battery longevity is only known for previous generation devices and whilst projected longevities are available for current devices, these are not in comparable formats. This study presents a new framework that facilitates an estimation of longevities for all CIEDs of both previous and existing generations that could simplify personalization of the device choice. Longevity can be calculated based upon a simple concept entitled the \"power consumption index\" (PCI = t x I/C, where t is a constant of 1 hour, I is the current required by the device and C, its battery capacity). We retrieved published data from the user manuals of all commonly used pacemakers including single chamber, dual chamber, cardiac resynchronization and leadless devices. C and the components of current I including background current (Ibackground) and the pacing current (Ipacing) were calculated prior to calculation of the PCI for each device. Subsequently, a set of fictitious patient pool conditions via a Monte-Carlo simulation were used to model CIED survival curves which were then compared with real-life data from the Swedish device registry of previous generation CIEDs. Finally, we modeled survival curves for current generation devices using the PCI model. Using the PCI approach we were able to calculate longevities for all pacemaker devices under a variety of settings. The modeled Ibackground matched the data reported by manufacturers, and, under a variety of settings, regression analysis showed a low average error rate between industry-reported and modelled longevities (ratio: modelled longevity/industry reported longevity -1) = 0.1 ± 4.0% and 0.1 ± 0.7% for previous and existing SR/DR devices, 1.0 ± 5.0% and 0 ± 3.0% for previous and existing CRT-P, and 0 ± 4.0% for leadless pacemakers, respectively). More than 50% of the PCI and thereby a significant contributor to longevity was accounted for by Ibackground. Ipacing was the second largest contributor (20% for standard single and dual chamber devices, 30% for CRT-P and 40% for leadless devices). Certain pacing algorithms and IEGM storage considerably impacted specific devices with longevity losses of up to 1 year. The Monte-Carlo analysis demonstrated consistency between projected longevities by the PCI model and real-life data for historical devices and the calculated longevities that stemmed from this were consistent with the real-world data from Sweden. The PCI model combining power consumption and battery capacity allows a comparison of longevity across CIEDs and programming options. Such a tool could help implanters improve personalization of device prescription for their patients and payers to make more informed decisions about tailoring device purchases and programming most appropriate for their population.

Aims Left ventricular (LV) thrombus is increasingly detected in patients with and without ischaemic heart disease due to the increased availability of cardiac magnetic resonance imaging. Risk factors include anterior ST elevation myocardial infarction, delayed reperfusion therapy, and non‐ischaemic cardiomyopathy with severe LV systolic dysfunction. We aimed to report the characteristics and outcomes of patients with LV thrombus treated with either vitamin K antagonist (VKA) or direct oral anticoagulants (DOAC) with a view to describing differences in efficacy, specifically, subsequent thromboembolic events, thrombus resolution, and also side effects of therapy including clinically significant bleeding. Methods and results We conducted a retrospective, observational cohort study of patients diagnosed with LV thrombus between 1 December 2012 and 30 June 2018 and treated with either DOAC or VKA. We recorded patient demographics, past medical history, prescribed medications, and baseline investigations. The primary outcomes were rates of thromboembolism and clinically significant bleeding, with secondary outcomes of thrombus resolution on repeat cardiac imaging, repeat hospitalization, and all‐cause mortality. During the study period, 84 patients were diagnosed with and managed for LV thrombus. Of these, 62 received VKA and 22 DOAC including 13 prescribed rivaroxaban, eight apixaban, and one dabigatran. Most patients 75 (89%) were male with an average age of 62 ± 14 years. Ischaemic heart disease was the cause of LV impairment in 73 (87%) patients. Baseline characteristics were similar between groups at baseline. Most n = 55 (65%) were co‐prescribed a single antiplatelet agent and 32 (38%) received dual‐antiplatelet therapy. During an average follow‐up of 3.0 ± 1.4 years, there were no statistically significant differences between VKA and DOAC in rates of stroke (2% vs. 0%, P = 0.55), other thromboemboli (2% vs. 0%, P = 0.55), or clinically significant bleeding (10% vs. 0%, P = 0.13). The average interval to cardiac imaging follow‐up was 233 ± 251 days and was not different between groups (P = 0.83), and there was no difference in the rate of resolution of thrombus (76% vs. 65% P = 0.33). Rehospitalization (50% vs. 45%: P = 0.53) and all‐cause mortality (10% vs. 14%; P = 0.61) were also similar. Conclusions Our data suggest that DOACs are likely to be at least as effective and safe as VKA for stroke prevention in patients with LV thrombus and, despite their lack of a licence for this indication, are therefore likely to represent a reasonable and more convenient option for this setting. The optimal timing and type of anticoagulation for LV thrombus, as well as the role of screening for high‐risk patients, should be tested in prospective, randomized trials.

Non-communicable diseases (NCDs) have been highlighted as important risk factors for COVID-19 mortality. However, insufficient data exist on the wider context of infectious diseases in people with NCDs. We aimed to investigate the association between NCDs and the risk of death from any infection before the COVID-19 pandemic (up to Dec 31, 2019). For this observational study, we used data from the UK Biobank observational cohort study to explore factors associated with infection death. We excluded participants if data were missing for comorbidities, body-mass index, smoking status, ethnicity, and socioeconomic deprivation, and if they were lost to follow-up or withdrew consent. Deaths were censored up to Dec 31, 2019. We used Poisson regression models including NCDs present at recruitment to the UK Biobank (obesity [defined by use of body-mass index] and self-reported hypertension, chronic heart disease, chronic respiratory disease, diabetes, cancer, chronic liver disease, chronic kidney disease, previous stroke or transient ischaemic attack, other neurological disease, psychiatric disorder, and chronic inflammatory and autoimmune rheumatological disease), age, sex, ethnicity, smoking status, and socioeconomic deprivation. Separate models were constructed with individual NCDs replaced by the total number of prevalent NCDs to define associations with multimorbidity. All analyses were repeated with non-infection-related death as an alternate outcome measure to establish differential associations of infection death and non-infection death. Associations are reported as incidence rate ratios (IRR) accompanied by 95% CIs. After exclusion of 9210 (1·8%) of the 502 505 participants in the UK Biobank cohort, our study sample comprised 493 295 individuals. During 5 273 731 person-years of follow-up (median 10·9 years [IQR 10·1–11·6] per participant), 27 729 deaths occurred, of which 1385 (5%) were related to infection. Advancing age, male sex, smoking, socioeconomic deprivation, and all studied NCDs were independently associated with the rate of both infection death and non-infection death. Compared with White ethnicity, a pooled Black, Asian, and minority ethnicity group was associated with a reduced risk of infection death (IRR 0·64, 95% CI 0·46–0·87) and non-infection death (0·80, 0·75–0·86). Stronger associations with infection death than with non-infection death were observed for advancing age (age 65 years vs 45 years: 7·59, 95% CI 5·92–9·73, for infection death vs 5·21, 4·97–5·48, for non-infection death), current smoking (vs never smoking: 3·69, 3·19–4·26, vs 2·52, 2·44–2·61), socioeconomic deprivation (most vs least deprived quintile: 2·13, 1·78–2·56, vs 1·38, 1·33–1·43), class 3 obesity (vs non-obese: 2·21, 1·74–2·82, vs 1·55, 1·44–1·66), hypertension (1·36, 1·22–1·53, vs 1·15, 1·12–1·18), respiratory disease (2·21, 1·96–2·50, vs 1·28, 1·24–1·32), chronic kidney disease (5·04, 4·28–7·31, vs 2·50, 2·20–2·84), psychiatric disease (1·56, 1·30–1·86, vs 1·23, 1·18–1·29), and chronic inflammatory and autoimmune rheumatological disease (2·45, 1·99–3·02, vs 1·41, 1·32–1·51). Accrual of multimorbidity was also more strongly associated with risk of infection death (five or more comorbidities vs none: 9·53, 6·97–13·03) than of non-infection death (5·26, 4·84–5·72). Several NCDs are associated with an increased risk of infection death, suggesting that some of the reported associations with COVID-19 mortality might be non-specific. Only a subset of NCDs, together with the accrual of multimorbidity, advancing age, smoking, and socioeconomic deprivation, were associated with a greater IRR for infection death than for other causes of death. Further research is needed to define why these risk factors are more strongly associated with infection death, so that more effective preventive strategies can be targeted to high-risk groups. British Heart Foundation.

In our experience, while patients often feel better, they rarely become asymptomatic (NYHA (New York Heart Association) class 1),12 an observation supported by real-world data even in those receiving ARNI.13 14 Moreover, we should consider whether a highly subjective and poorly reproducible assessment is appropriate to determine our allocation of life-saving treatments.15 Hence, criteria requiring repeat assessment act as a barrier to initiating additional therapies such as MRA or ARNI,7 which are regarded as ‘second-line’ due to the hierarchical framework which places greater emphasis on therapies based on the chronological sequence in which the trials were performed. There is no logical basis to assume that drug classes trialled earliest would be the most beneficial, yet this is what guidelines imply. [...]if we are to make progress, future guidelines must address these limitations and incorporate the Four Pillars of Heart Failure into a comprehensive disease modifying programme for all people living with HFrEF. Furthermore, the comparator was a submaximal dose of enalapril compounded by lower blood pressure in those allocated ARNI suggesting undertreatment in the control arm.16 It has also been suggested that the trial was additionally biased in favour of the novel agent due to a double drug run-in period of unequal times, in which those randomised to ARNI had already received an ACEi and were therefore pre-selected (20% of patients were lost during the run-in period).17 Drug doses are related to outcomes in HFrEF18–20 and the HR for the composite outcome in PARADIGM-HF between sacubitril-valsartan and enalapril was similar to the comparison of high and low dosing of lisinopril in the ATLAS trial.21 However, post-hoc analysis has shown that the point estimates for the benefit of low dose ARNI compared with low dose ACEi were identical to the point estimate of the overall trial,22 and real-world data have shown clear improvements in outcomes, symptoms and quality of life compared with standard of care ACEi.23 Another difficulty employing ARNI across the board includes the wash-out period required following cessation of ACEi due to risks of angioedema. [...]if the benefits of the activity are perceived (whether correctly or incorrectly) to be minimal, physician inertia may prevail.

In the current global climate models (GCMs), the nonlinearity effect of subgrid cloud variations on the parameterization of warm-rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm-rain process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content (qc) and cloud droplet number concentration (Nc) in marine boundary layer (MBL) clouds based on the in situ measurements from a recent field campaign and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that tends to make EF < 1. This effect is especially strong at cloud top, where the qc and Nc correlation can be as high as 0.95. We also found that the physically complete EF that accounts for the covariation of qc and Nc is significantly smaller than its counterpart that accounts only for the subgrid variation of qc, especially at cloud top. Although this study is based on limited cases, it suggests that the subgrid variations of Nc and its correlation with qc both need to be considered for an accurate simulation of the autoconversion process in GCMs.

Chronotropic incompetence (CI) is an inability to adequately raise heart rate during physiological stress. We established CI prevalence and exercise capacity in heart failure versus healthy age-matched controls. We conducted a systematic search (1966–July 1, 2020) and meta-analysis of studies reporting peak VO2 in people with heart failure with reduced (HFrEF) and preserved (HFpEF) left ventricular ejection fraction and controls. Seventeen studies of 4410 participants were included, 4167 with heart failure and 243 age-matched controls. In both heart failure phenotypes, CI was more prevalent in HFrEF (51.7%) and HFpEF (55.8%) than in healthy controls (9%). Mortality was 24% higher in people with HFrEF and CI versus those with HFrEF and without CI; OR −1.24 (95% CI −2.20 to −0.28; p = 0.01). People with heart failure and CI had lower peak VO2 than those without CI (MD) −3.30 ml kg−1 min−1 (95% CI −4.25 to −2.35, p < 0.01), and this was primarily driven by the HFrEF sub-population (MD) −3.86 ml kg−1 min−1 (95% CI −4.83 to −2.89, p < 0.01). Maximum heart rate MD −37.51 beats min−1 (95% CI −41.99 to −33.03, p < 0.01) and maximum-resting heart rate were lower MD −29.44 beats min−1 (95% CI −34.55 to −24.33, p < 0.01) in people with heart failure with CI vs without CI. People with heart failure and CI demonstrated similar respiratory exchange ratios (RER) to people with heart failure but without CI; (MD) −0.02 (95% CI −0.03 to −0.01), p < 0.01, suggesting that poor effort was unlikely to explain CI. CI is more prevalent in heart failure than in age-matched controls and although it is associated with lower peak VO2 in HFrEF, it is unrelated to the lower peak VO2 in HFpEF. RER values suggest poor effort is unlikely to explain these findings.

The endoplasmic reticulum (ER) regulates cellular protein and lipid biosynthesis. ER dysfunction leads to protein misfolding and the unfolded protein response (UPR), which limits protein synthesis to prevent cytotoxicity. Chronic ER stress in skeletal muscle is a unifying mechanism linking lipotoxicity to metabolic disease. Unidentified signals from cells undergoing ER stress propagate paracrine and systemic UPR activation. Here, we induce ER stress and lipotoxicity in myotubes. We observe ER stress-inducing lipid cell non-autonomous signal(s). Lipidomics identifies that palmitate-induced cell stress induces long-chain ceramide 40:1 and 42:1 secretion. Ceramide synthesis through the ceramide synthase 2 de novo pathway is regulated by UPR kinase Perk. Inactivation of CerS2 in mice reduces systemic and muscle ceramide signals and muscle UPR activation. The ceramides are packaged into extracellular vesicles, secreted and induce UPR activation in naïve myotubes through dihydroceramide accumulation. This study furthers our understanding of ER stress by identifying UPR-inducing cell non-autonomous signals. Endoplasmic Reticulum stress induces cell non-autonomous Unfolded Protein Response (UPR) activation. Here the authors show that long-chain ceramides are secreted from muscle cells in extracellular vesicles and induce cell non-autonomous UPR activation in muscle cells in response to lipotoxcity.