SummaryBackgroundLarge-scale and contemporary population-based studies of heart failure incidence are needed to inform resource planning and research prioritisation but current evidence is scarce. We aimed to assess temporal trends in incidence and prevalence of heart failure in a large general population cohort from the UK, between 2002 and 2014.MethodsFor this population-based study, we used linked primary and secondary electronic health records of 4 million individuals from the Clinical Practice Research Datalink (CPRD), a cohort that is representative of the UK population in terms of age and sex. Eligible patients were aged 16 years and older, had contributed data between Jan 1, 2002, and Dec 31, 2014, had an acceptable record according to CPRD quality control, were approved for CPRD and Hospital Episodes Statistics linkage, and were registered with their general practice for at least 12 months. For patients with incident heart failure, we extracted the most recent measurement of baseline characteristics (within 2 years of diagnosis) from electronic health records, as well as information about comorbidities, socioeconomic status, ethnicity, and region. We calculated standardised rates by applying direct age and sex standardisation to the 2013 European Standard Population, and we inferred crude rates by applying year-specific, age-specific, and sex-specific incidence to UK census mid-year population estimates. We assumed no heart failure for patients aged 15 years or younger and report total incidence and prevalence for all ages (>0 years).FindingsFrom 2002 to 2014, heart failure incidence (standardised by age and sex) decreased, similarly for men and women, by 7% (from 358 to 332 per 100 000 person-years; adjusted incidence ratio 0·93, 95% CI 0·91–0·94). However, the estimated absolute number of individuals with newly diagnosed heart failure in the UK increased by 12% (from 170 727 in 2002 to 190 798 in 2014), largely due to an increase in population size and age. The estimated absolute number of prevalent heart failure cases in the UK increased even more, by 23% (from 750 127 to 920 616). Over the study period, patient age and multi-morbidity at first presentation of heart failure increased (mean age 76·5 years [SD 12·0] to 77·0 years [12·9], adjusted difference 0·79 years, 95% CI 0·37–1·20; mean number of comorbidities 3·4 [SD 1·9] vs 5·4 [2·5]; adjusted difference 2·0, 95% CI 1·9–2·1). Socioeconomically deprived individuals were more likely to develop heart failure than were affluent individuals (incidence rate ratio 1·61, 95% CI 1·58–1·64), and did so earlier in life than those from the most affluent group (adjusted difference −3·51 years, 95% CI −3·77 to −3·25). From 2002 to 2014, the socioeconomic gradient in age at first presentation with heart failure widened. Socioeconomically deprived individuals also had more comorbidities, despite their younger age.InterpretationDespite a moderate decline in standardised incidence of heart failure, the burden of heart failure in the UK is increasing, and is now si...

Whether anxiety is a risk factor for a range of cardiovascular diseases is unclear. We aimed to determine the association between anxiety and a range of cardiovascular diseases. MEDLINE and EMBASE were searched for cohort studies that included participants with and without anxiety, including subjects with anxiety, worry, posttraumatic stress disorder, phobic anxiety, and panic disorder. We examined the association of anxiety with cardiovascular mortality, major cardiovascular events (defined as the composite of cardiovascular death, stroke, coronary heart disease, and heart failure), stroke, coronary heart disease, heart failure, and atrial fibrillation. We identified 46 cohort studies containing 2,017,276 participants and 222,253 subjects with anxiety. Anxiety was associated with a significantly elevated risk of cardiovascular mortality (relative risk [RR] 1.41, CI 1.13 to 1.76), coronary heart disease (RR 1.41, CI 1.23 to 1.61), stroke (RR 1.71, CI 1.18 to 2.50), and heart failure (RR 1.35, CI 1.11 to 1.64). Anxiety was not significantly associated with major cardiovascular events or atrial fibrillation although CIs were wide. Phobic anxiety was associated with a higher risk of coronary heart disease than other anxiety disorders, and posttraumatic stress disorder was associated with a higher risk of stroke. Results were broadly consistent in sensitivity analyses. Anxiety disorders are associated with an elevated risk of a range of different cardiovascular events, including stroke, coronary heart disease, heart failure, and cardiovascular death. Whether these associations are causal is unclear.

BackgroundMultimorbidity in people with cardiovascular disease (CVD) is common, but large-scale contemporary reports of patterns and trends in patients with incident CVD are limited. We investigated the burden of comorbidities in patients with incident CVD, how it changed between 2000 and 2014, and how it varied by age, sex, and socioeconomic status (SES).Methods and findingsWe used the UK Clinical Practice Research Datalink with linkage to Hospital Episode Statistics, a population-based dataset from 674 UK general practices covering approximately 7% of the current UK population. We estimated crude and age/sex-standardised (to the 2013 European Standard Population) prevalence and 95% confidence intervals for 56 major comorbidities in individuals with incident non-fatal CVD. We further assessed temporal trends and patterns by age, sex, and SES groups, between 2000 and 2014. Among a total of 4,198,039 people aged 16 to 113 years, 229,205 incident cases of non-fatal CVD, defined as first diagnosis of ischaemic heart disease, stroke, or transient ischaemic attack, were identified. Although the age/sex-standardised incidence of CVD decreased by 34% between 2000 to 2014, the proportion of CVD patients with higher numbers of comorbidities increased. The prevalence of having 5 or more comorbidities increased 4-fold, rising from 6.3% (95% CI 5.6%–17.0%) in 2000 to 24.3% (22.1%–34.8%) in 2014 in age/sex-standardised models. The most common comorbidities in age/sex-standardised models were hypertension (28.9% [95% CI 27.7%–31.4%]), depression (23.0% [21.3%–26.0%]), arthritis (20.9% [19.5%–23.5%]), asthma (17.7% [15.8%–20.8%]), and anxiety (15.0% [13.7%–17.6%]). Cardiometabolic conditions and arthritis were highly prevalent among patients aged over 40 years, and mental illnesses were highly prevalent in patients aged 30–59 years. The age-standardised prevalence of having 5 or more comorbidities was 19.1% (95% CI 17.2%–22.7%) in women and 12.5% (12.0%–13.9%) in men, and women had twice the age-standardised prevalence of depression (31.1% [28.3%–35.5%] versus 15.0% [14.3%–16.5%]) and anxiety (19.6% [17.6%–23.3%] versus 10.4% [9.8%–11.8%]). The prevalence of depression was 46% higher in the most deprived fifth of SES compared with the least deprived fifth (age/sex-standardised prevalence of 38.4% [31.2%–62.0%] versus 26.3% [23.1%–34.5%], respectively). This is a descriptive study of routine electronic health records in the UK, which might underestimate the true prevalence of diseases.ConclusionsThe burden of multimorbidity and comorbidity in patients with incident non-fatal CVD increased between 2000 and 2014. On average, older patients, women, and socioeconomically deprived groups had higher numbers of comorbidities, but the type of comorbidities varied by age and sex. Cardiometabolic conditions contributed substantially to the burden, but 4 out of the 10 top comorbidities were non-cardiometabolic. The current single-disease paradigm in CVD management needs to broaden and incorporate the large and increasing burden of comorbid...

IMPORTANCE Despite considerable improvements in heart failure care, mortality rates among patients in high-income countries have changed little since the early 2000s. Understanding the reasons underlying these trends may provide valuable clues for developing more targeted therapies and public health strategies. OBJECTIVE To investigate mortality rates following a new diagnosis of heart failure and examine changes over time and by cause of death and important patient features. DESIGN, SETTING, AND PARTICIPANTS This population-based retrospective cohort study analyzed anonymized electronic health records of individuals who received a new diagnosis of heart failure between January 2002 and December 2013 who were followed up until December 2014 from the Clinical Practice Research Datalink, which links information from primary care, secondary care, and the national death registry from a subset of the UK population. The data were analyzed from January 2018 to February 2019.MAIN OUTCOMES AND MEASURES All-cause and cause-specific mortality rates at 1 year following diagnosis. Poisson regression models were used to calculate rate ratios (RRs) and 95% confidence intervals comparing 2013 with 2002, adjusting for age, sex, region, socioeconomic status, and 17 major comorbidities. RESULTSOf 86 833 participants, 42 581 (49%) were women, 51 215 (88%) were white, and the mean (SD) age was 76.6 (12.6) years. While all-cause mortality rates declined only modestly over time (RR comparing 2013 with 2002, 0.94; 95% CI, 0.88-1.00), underlying patterns presented explicit trends. A decline in cardiovascular mortality (RR, 0.73; 95% CI, 0.67-0.80) was offset by an increase in noncardiovascular deaths (RR, 1.22; 95% CI, 1.11-1.33). Subgroup analyses further showed that overall mortality rates declined among patients younger than 80 years (RR, 0.79; 95% CI, 0.71-0.88) but not among those older than 80 years (RR, 0.97; 95% CI, 0.90-1.06). After cardiovascular causes (898 [43%]), the major causes of death in 2013 were neoplasms (311 [15%]), respiratory conditions (243 [12%]), and infections (13%), the latter 2 explaining most of the observed increase in noncardiovascular mortality. CONCLUSIONS AND RELEVANCE Among patients with a new heart failure diagnosis, considerable progress has been achieved in reducing mortality in young and middle-aged patients and cardiovascular mortality across all age groups. Improvements to overall mortality are hindered by high and increasing rates of noncardiovascular events. These findings challenge current research priorities and management strategies and call for a greater emphasis on associated comorbidities. Specifically, infection prevention presents as a major opportunity to improve prognosis.

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BackgroundEmergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one.Methods and findingsWe used longitudinal data from linked electronic health records of 4.6 million patients aged 18–100 years from 389 practices across England between 1985 to 2015. The population was divided into a derivation cohort (80%, 3.75 million patients from 300 general practices) and a validation cohort (20%, 0.88 million patients from 89 general practices) from geographically distinct regions with different risk levels. We first replicated a previously reported Cox proportional hazards (CPH) model for prediction of the risk of the first emergency admission up to 24 months after baseline. This reference model was then compared with 2 machine learning models, random forest (RF) and gradient boosting classifier (GBC). The initial set of predictors for all models included 43 variables, including patient demographics, lifestyle factors, laboratory tests, currently prescribed medications, selected morbidities, and previous emergency admissions. We then added 13 more variables (marital status, prior general practice visits, and 11 additional morbidities), and also enriched all variables by incorporating temporal information whenever possible (e.g., time since first diagnosis). We also varied the prediction windows to 12, 36, 48, and 60 months after baseline and compared model performances. For internal validation, we used 5-fold cross-validation. When the initial set of variables was used, GBC outperformed RF and CPH, with an area under the receiver operating characteristic curve (AUC) of 0.779 (95% CI 0.777, 0.781), compared to 0.752 (95% CI 0.751, 0.753) and 0.740 (95% CI 0.739, 0.741), respectively. In external validation, we observed an AUC of 0.796, 0.736, and 0.736 for GBC, RF, and CPH, respectively. The addition of temporal information improved AUC across all models. In internal validation, the AUC rose to 0.848 (95% CI 0.847, 0.849), 0.825 (95% CI 0.824, 0.826), and 0.805 (95% CI 0.804, 0.806) for GBC, RF, and CPH, respectively, while the AUC in external validation rose to 0.826, 0.810, and 0.788, respectively. This enhancement also resulted in robust predictions for longer time horizons, with AUC values remaining at similar levels across all models. Overall, compared to the baseline reference CPH model, the final GBC model showed a 10.8% higher AUC (0.848 compared to 0.740) for prediction of risk of emergency admission within 24 months. GBC also showed the best calibration throughout the risk spectrum. Despite the wide range of variables included in models, our study was still limited by the number of variables included; inclusion of more variables could have further improved model performances.ConclusionsThe use of machine learning and additio...

Ewing sarcomas are characterized by the presence of EWS/ETS fusion genes in the absence of other recurrent genetic alterations and mechanisms of tumor heterogeneity that contribute to disease progression remain unclear. Mutations in the Wnt/beta-catenin pathway are rare in Ewing sarcoma but the Wnt pathway modulator LGR5 is often highly expressed, suggesting a potential role for the axis in tumor pathogenesis. We evaluated beta-catenin and LGR5 expression in Ewing sarcoma cell lines and tumors and noted marked intra- and inter-tumor heterogeneity. Tumors with evidence of active Wnt/beta-catenin signaling were associated with increased incidence of tumor relapse and worse overall survival. Paradoxically, RNA sequencing revealed a marked antagonism of EWS/ETS transcriptional activity in Wnt/beta-catenin activated tumor cells. Consistent with this, Wnt/beta-catenin activated cells displayed a phenotype that was reminiscent of Ewing sarcoma cells with partial EWS/ETS loss of function. Specifically, activation of Wnt/beta-catenin induced alterations to the actin cytoskeleton, acquisition of a migratory phenotype and up regulation of EWS/ETS-repressed genes. Notably, activation of Wnt/beta-catenin signaling led to marked induction of tenascin C (TNC), an established promoter of cancer metastasis, and an EWS/ETS-repressed target gene. Loss of TNC function in Ewing sarcoma cells profoundly inhibited their migratory and metastatic potential. Our studies reveal that heterogeneous activation of Wnt/beta-catenin signaling in subpopulations of tumor cells contributes to phenotypic heterogeneity and disease progression in Ewing sarcoma. Significantly, this is mediated, at least in part, by inhibition of EWS/ETS fusion protein function that results in de-repression of metastasis-associated gene programs.

Mutations in dysferlin cause an inherited muscular dystrophy because of defective membrane repair. Three interacting partners of dysferlin are also implicated in membrane resealing: caveolin-3 (in limb girdle muscular dystrophy type 1C), annexin A1, and the newly identified protein mitsugumin 53 (MG53). Mitsugumin 53 accumulates at sites of membrane damage, and MG53-knockout mice display a progressive muscular dystrophy. This study explored the expression and localization of MG53 in human skeletal muscle, how membrane repair proteins are modulated in various forms of muscular dystrophy, and whether MG53 is a primary cause of human muscle disease. Mitsugumin 53 showed variable sarcolemmal and/or cytoplasmic immunolabeling in control human muscle and elevated levels in dystrophic patients. No pathogenic MG53 mutations were identified in 50 muscular dystrophy patients, suggesting that MG53 is unlikely to be a common cause of muscular dystrophy in Australia. Western blot analysis confirmed upregulation of MG53, as well as of dysferlin, annexin A1, and caveolin-3 to different degrees, in different muscular dystrophies. Importantly, MG53, annexin A1, and dysferlin localize to the t-tubule network and show enriched labeling at longitudinal tubules of the t-system in overstretch. Our results suggest that longitudinal tubules of the t-system may represent sites of physiological membrane damage targeted by this membrane repair complex.