Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study

AIMS: Aortic valve stenosis is commonly considered a degenerative disorder with no recommended preventive intervention, with only valve replacement surgery or catheter intervention as treatment options. We sought to assess the causal association between exposure to lipid levels and risk of aortic stenosis. METHODS AND RESULTS: Causality of association was assessed using two-sample Mendelian randomization framework through different statistical methods. We retrieved summary estimations of 157 genetic variants that have been shown to be associated with plasma lipid levels in the Global Lipids Genetics Consortium that included 188 577 participants, mostly European ancestry, and genetic association with aortic stenosis as the main outcome from a total of 432 173 participants in the UK Biobank. Secondary negative control outcomes included aortic regurgitation and mitral regurgitation. The odds ratio for developing aortic stenosis per unit increase in lipid parameter was 1.52 [95% confidence interval (CI) 1.22-1.90; per 0.98 mmol/L] for low density lipoprotein (LDL)-cholesterol, 1.03 (95% CI 0.80-1.31; per 0.41 mmol/L) for high density lipoprotein (HDL)-cholesterol, and 1.38 (95% CI 0.92-2.07; per 1 mmol/L) for triglycerides. There was no evidence of a causal association between any of the lipid parameters and aortic or mitral regurgitation. CONCLUSION: Lifelong exposure to high LDL-cholesterol increases the risk of symptomatic aortic stenosis, suggesting that LDL-lowering treatment may be effective in its prevention

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

Multimessenger NuEM Alerts with AMON

The Astrophysical Multimessenger Observatory Network (AMON), has developed a real-time multi-messenger alert system. The system performs coincidence analyses of datasets from gamma-ray and neutrino detectors, making the Neutrino-Electromagnetic (NuEM) alert channel. For these analyses, AMON takes advantage of sub-threshold events, i.e., events that by themselves are not significant in the individual detectors. The main purpose of this channel is to search for gamma-ray counterparts of neutrino events. We will describe the different analyses that make-up this channel and present a selection of recent results

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Systolic blood pressure and risk of valvular heart disease: A mendelian randomization study

Importance Modifiable risk factors for valvular heart disease remain largely unknown, which limits prevention and treatment. Objective To assess the association between systolic blood pressure (BP) and major valvular heart disease. Design, Setting, and Participants A UK Biobank population-based cohort of 502 602 men and women aged 40 to 96 years at baseline was evaluated through mendelian randomization using individual participant data. Inclusion criteria were valid genetic data and BP measurements. The participants were recruited between 2006 and 2010; data analysis was performed from June 2018 to January 2019. Exposures Systolic BP was measured during clinical assessment and instruments for the genetic effect of high BP were identified from variants that were independently (linkage disequilibrium threshold of r2≺0.1) associated with systolic BP with minor allele frequency greater than 0.01. A total of 130 single-nucleotide polymorphisms that have been shown to be associated with systolic BP in a genome-wide association meta-analysis involving 1 million participants of European ancestry were selected. Main Outcomes and Measures Incident aortic stenosis, aortic regurgitation, and mitral regurgitation, individually and combined. Cases were largely based on hospital records linked to the UK Biobank with International Classification of Diseases and Health Related Problems, Tenth Revision codes. Results Of the 502 602 individuals screened, 329 237 participants (177 741 [53.99%] women; mean [SD] age, 56.93 [7.99] years) had valid genetic data and BP measurements; of this cohort, 3570 individuals (1.08%) had a diagnosis of valvular heart disease (aortic stenosis, 1491 [0.45%]; aortic regurgitation, 634 [0.19%]; and mitral regurgitation, 1736 [0.53%]). Each genetically associated 20–mm Hg increment in systolic BP was associated with an increased risk of aortic stenosis (odds ratio [OR], 3.26; 95% CI, 1.50-7.10), aortic regurgitation (OR, 2.59; 95% CI, 0.75-8.92), and mitral regurgitation (OR, 2.19; 95% CI, 1.07-4.47), with no evidence for heterogeneity by type of valvular heart disease (P = .90). Sensitivity analyses confirmed the robustness of the association. Conclusions and Relevance Lifetime exposure to elevated systolic BP appears to be associated with an increased risk of major valvular heart disease.</p

Systolic blood pressure and risk of valvular heart disease: A mendelian randomization study

Importance Modifiable risk factors for valvular heart disease remain largely unknown, which limits prevention and treatment. Objective To assess the association between systolic blood pressure (BP) and major valvular heart disease. Design, Setting, and Participants A UK Biobank population-based cohort of 502 602 men and women aged 40 to 96 years at baseline was evaluated through mendelian randomization using individual participant data. Inclusion criteria were valid genetic data and BP measurements. The participants were recruited between 2006 and 2010; data analysis was performed from June 2018 to January 2019. Exposures Systolic BP was measured during clinical assessment and instruments for the genetic effect of high BP were identified from variants that were independently (linkage disequilibrium threshold of r2≺0.1) associated with systolic BP with minor allele frequency greater than 0.01. A total of 130 single-nucleotide polymorphisms that have been shown to be associated with systolic BP in a genome-wide association meta-analysis involving 1 million participants of European ancestry were selected. Main Outcomes and Measures Incident aortic stenosis, aortic regurgitation, and mitral regurgitation, individually and combined. Cases were largely based on hospital records linked to the UK Biobank with International Classification of Diseases and Health Related Problems, Tenth Revision codes. Results Of the 502 602 individuals screened, 329 237 participants (177 741 [53.99%] women; mean [SD] age, 56.93 [7.99] years) had valid genetic data and BP measurements; of this cohort, 3570 individuals (1.08%) had a diagnosis of valvular heart disease (aortic stenosis, 1491 [0.45%]; aortic regurgitation, 634 [0.19%]; and mitral regurgitation, 1736 [0.53%]). Each genetically associated 20–mm Hg increment in systolic BP was associated with an increased risk of aortic stenosis (odds ratio [OR], 3.26; 95% CI, 1.50-7.10), aortic regurgitation (OR, 2.59; 95% CI, 0.75-8.92), and mitral regurgitation (OR, 2.19; 95% CI, 1.07-4.47), with no evidence for heterogeneity by type of valvular heart disease (P = .90). Sensitivity analyses confirmed the robustness of the association. Conclusions and Relevance Lifetime exposure to elevated systolic BP appears to be associated with an increased risk of major valvular heart disease.</p

Systolic blood pressure and risk of valvular heart disease: a mendelian randomization study

Importance Modifiable risk factors for valvular heart disease remain largely unknown, which limits prevention and treatment. Objective To assess the association between systolic blood pressure (BP) and major valvular heart disease. Design, Setting, and Participants A UK Biobank population-based cohort of 502 602 men and women aged 40 to 96 years at baseline was evaluated through mendelian randomization using individual participant data. Inclusion criteria were valid genetic data and BP measurements. The participants were recruited between 2006 and 2010; data analysis was performed from June 2018 to January 2019. Exposures Systolic BP was measured during clinical assessment and instruments for the genetic effect of high BP were identified from variants that were independently (linkage disequilibrium threshold of r2&lt;0.1) associated with systolic BP with minor allele frequency greater than 0.01. A total of 130 single-nucleotide polymorphisms that have been shown to be associated with systolic BP in a genome-wide association meta-analysis involving 1 million participants of European ancestry were selected. Main Outcomes and Measures Incident aortic stenosis, aortic regurgitation, and mitral regurgitation, individually and combined. Cases were largely based on hospital records linked to the UK Biobank with International Classification of Diseases and Health Related Problems, Tenth Revision codes. Results Of the 502 602 individuals screened, 329 237 participants (177 741 [53.99%] women; mean [SD] age, 56.93 [7.99] years) had valid genetic data and BP measurements; of this cohort, 3570 individuals (1.08%) had a diagnosis of valvular heart disease (aortic stenosis, 1491 [0.45%]; aortic regurgitation, 634 [0.19%]; and mitral regurgitation, 1736 [0.53%]). Each genetically associated 20–mm Hg increment in systolic BP was associated with an increased risk of aortic stenosis (odds ratio [OR], 3.26; 95% CI, 1.50-7.10), aortic regurgitation (OR, 2.59; 95% CI, 0.75-8.92), and mitral regurgitation (OR, 2.19; 95% CI, 1.07-4.47), with no evidence for heterogeneity by type of valvular heart disease (P = .90). Sensitivity analyses confirmed the robustness of the association. Conclusions and Relevance Lifetime exposure to elevated systolic BP appears to be associated with an increased risk of major valvular heart disease.</p

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records

Background Emergency 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 findings We 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. Conclusions The use of machine learning and addition of temporal information led to substantially improved discrimination and calibration for predicting the risk of emergency admission. Model performance remained stable across a range of prediction time windows and when externally validated. These findings support the potential of incorporating machine learning models into electronic health records to inform care and service planning

Multi-morbidity and blood pressure trajectories in hypertensive patients: a multiple landmark cohort study

Background Our knowledge of how to better manage elevated blood pressure (BP) in the presence of comorbidities is limited, in part due to exclusion or underrepresentation of patients with multiple chronic conditions from major clinical trials. We aimed to investigate the burden and types of comorbidities in patients with hypertension and to assess how such comorbidities and other variables affect BP levels over time. Methods and findings In this multiple landmark cohort study, we used linked electronic health records from the United Kingdom Clinical Practice Research Datalink (CPRD) to compare systolic blood pressure (SBP) levels in 295,487 patients (51% women) aged 61.5 (SD = 13.1) years with first recorded diagnosis of hypertension between 2000 and 2014, by type and numbers of major comorbidities, from at least 5 years before and up to 10 years after hypertension diagnosis. Time-updated multivariable linear regression analyses showed that the presence of more comorbidities was associated with lower SBP during follow-up. In hypertensive patients without comorbidities, mean SBP at diagnosis and at 10 years were 162.3 mm Hg (95% confidence interval [CI] 162.0 to 162.6) and 140.5 mm Hg (95% CI 140.4 to 140.6), respectively; in hypertensive patients with ≥5 comorbidities, these were 157.3 mm Hg (95% CI 156.9 to 157.6) and 136.8 mm Hg (95% 136.4 to 137.3), respectively. This inverse association between numbers of comorbidities and SBP was not specific to particular types of comorbidities, although associations were stronger in those with preexisting cardiovascular disease. Retrospective analysis of recorded SBP showed that the difference in mean SBP 5 years before diagnosis between those without and with ≥5 comorbidities was −9 mm Hg (95% CI −9.7 to −8.3), suggesting that mean recorded SBP already differed according to the presence of comorbidity before baseline. Within 1 year after the diagnosis, SBP substantially declined, but subsequent SBP changes across comorbidity status were modest, with no evidence of a more rapid decline in those with more or specific types of comorbidities. We identified factors, such as prescriptions of antihypertensive drugs and frequency of healthcare visits, that can explain SBP differences according to numbers or types of comorbidities, but these factors only partly explained the recorded SBP differences. Nevertheless, some limitations have to be considered including the possibility that diagnosis of some conditions may not have been recorded, varying degrees of missing data inherent in analytical datasets extracted from routine health records, and greater measurement errors in clinical measurements taken in routine practices than those taken in well-controlled clinical study settings. Conclusions BP levels at which patients were diagnosed with hypertension varied substantially according to the presence of comorbidities and were lowest in patients with multi-morbidity. Our findings suggest that this early selection bias of hypertension diagnosis at different BP levels was a key determinant of long-term differences in BP by comorbidity status. The lack of a more rapid decline in SBP in those with multi-morbidity provides some reassurance for BP treatment in these high-risk individuals