Thermal and electrical conductivity of iron at Earth's core conditions

The Earth acts as a gigantic heat engine driven by decay of radiogenic isotopes and slow cooling, which gives rise to plate tectonics, volcanoes, and mountain building. Another key product is the geomagnetic field, generated in the liquid iron core by a dynamo running on heat released by cooling and freezing to grow the solid inner core, and on chemical convection due to light elements expelled from the liquid on freezing. The power supplied to the geodynamo, measured by the heat-flux across the core-mantle boundary (CMB), places constraints on Earth's evolution. Estimates of CMB heat-flux depend on properties of iron mixtures under the extreme pressure and temperature conditions in the core, most critically on the thermal and electrical conductivities. These quantities remain poorly known because of inherent difficulties in experimentation and theory. Here we use density functional theory to compute these conductivities in liquid iron mixtures at core conditions from first principles- the first directly computed values that do not rely on estimates based on extrapolations. The mixtures of Fe, O, S, and Si are taken from earlier work and fit the seismologically-determined core density and inner-core boundary density jump. We find both conductivities to be 2-3 times higher than estimates in current use. The changes are so large that core thermal histories and power requirements must be reassessed. New estimates of adiabatic heat-flux give 15-16 TW at the CMB, higher than present estimates of CMB heat-flux based on mantle convection; the top of the core must be thermally stratified and any convection in the upper core driven by chemical convection against the adverse thermal buoyancy or lateral variations in CMB heat flow. Power for the geodynamo is greatly restricted and future models of mantle evolution must incorporate a high CMB heat-flux and explain recent formation of the inner core.Comment: 11 pages including supplementary information, two figures. Scheduled to appear in Nature, April 201

Heart failure and major haemorrhage in people with atrial fibrillation

Background: Heart failure (HF) is not included in atrial fibrillation (AF) bleeding risk prediction scores, reflecting uncertainty regarding its importance as a risk factor for major haemorrhage. We aimed to report the relative risk of first major haemorrhage in people with HF and AF compared with people with AF without HF (‘AF only’). Methods: English primary care cohort study of 2 178 162 people aged ≥45 years in the Clinical Practice Research Datalink from January 2000 to December 2018, linked to secondary care and mortality databases. We used traditional survival analysis and competing risks methods, accounting for all-cause mortality and anticoagulation. Results: Over 7.56 years median follow-up, 60 270 people were diagnosed with HF and AF of whom 4996 (8.3%) had a major haemorrhage and 36 170 died (60.0%), compared with 8256 (6.4%) and 34 375 (27.2%), respectively, among 126 251 people with AF only. Less than half those with AF were prescribed an anticoagulant (45.6% from 2014 onwards), although 75.7% were prescribed an antiplatelet or anticoagulant. In a fully adjusted Cox model, the HR for major haemorrhage was higher among people with HF and AF (2.52, 95% CI 2.44 to 2.61) than AF only (1.87, 95% CI 1.82 to 1.92), even in a subgroup analysis of people prescribed anticoagulation. However, in a Fine and Gray competing risk model, the HR of major haemorrhage was similar for people with AF only (1.82, 95% CI 1.77 to 1.87) or HF and AF (1.71, 95% CI 1.66 to 1.78). Conclusions: People with HF and AF are at increased risk of major haemorrhage compared with those with AF only and current prediction scores may underestimate the risk of haemorrhage in HF and AF. However, people with HF and AF are more likely to die than have a major haemorrhage and therefore an individual’s expected prognosis should be carefully considered when predicting future bleeding risk

Heart failure and major haemorrhage in people with atrial fibrillation

Background: Heart failure (HF) is not included in atrial fibrillation (AF) bleeding risk prediction scores, reflecting uncertainty regarding its importance as a risk factor for major haemorrhage. We aimed to report the relative risk of first major haemorrhage in people with HF and AF compared with people with AF without HF (‘AF only’). Methods: English primary care cohort study of 2 178 162 people aged ≥45 years in the Clinical Practice Research Datalink from January 2000 to December 2018, linked to secondary care and mortality databases. We used traditional survival analysis and competing risks methods, accounting for all-cause mortality and anticoagulation. Results: Over 7.56 years median follow-up, 60 270 people were diagnosed with HF and AF of whom 4996 (8.3%) had a major haemorrhage and 36 170 died (60.0%), compared with 8256 (6.4%) and 34 375 (27.2%), respectively, among 126 251 people with AF only. Less than half those with AF were prescribed an anticoagulant (45.6% from 2014 onwards), although 75.7% were prescribed an antiplatelet or anticoagulant. In a fully adjusted Cox model, the HR for major haemorrhage was higher among people with HF and AF (2.52, 95% CI 2.44 to 2.61) than AF only (1.87, 95% CI 1.82 to 1.92), even in a subgroup analysis of people prescribed anticoagulation. However, in a Fine and Gray competing risk model, the HR of major haemorrhage was similar for people with AF only (1.82, 95% CI 1.77 to 1.87) or HF and AF (1.71, 95% CI 1.66 to 1.78). Conclusions: People with HF and AF are at increased risk of major haemorrhage compared with those with AF only and current prediction scores may underestimate the risk of haemorrhage in HF and AF. However, people with HF and AF are more likely to die than have a major haemorrhage and therefore an individual’s expected prognosis should be carefully considered when predicting future bleeding risk

Genomic variation landscape of the human gut microbiome

While large-scale efforts have rapidly advanced the understanding and practical impact of human genomic variation, the latter is largely unexplored in the human microbiome. We therefore developed a framework for metagenomic variation analysis and applied it to 252 fecal metagenomes of 207 individuals from Europe and North America. Using 7.4 billion reads aligned to 101 reference species, we detected 10.3 million single nucleotide polymorphisms (SNPs), 107,991 short indels, and 1,051 structural variants. The average ratio of non-synonymous to synonymous polymorphism rates of 0.11 was more variable between gut microbial species than across human hosts. Subjects sampled at varying time intervals exhibited individuality and temporal stability of SNP variation patterns, despite considerable composition changes of their gut microbiota. This implies that individual-specific strains are not easily replaced and that an individual might have a unique metagenomic genotype, which may be exploitable for personalized diet or drug intake

Impact of Changes to National Hypertension Guidelines on Hypertension Management and Outcomes in the United Kingdom.

In recent years, national and international guidelines have recommended the use of out-of-office blood pressure monitoring for diagnosing hypertension. Despite evidence of cost-effectiveness, critics expressed concerns this would increase cardiovascular morbidity. We assessed the impact of these changes on the incidence of hypertension, out-of-office monitoring and cardiovascular morbidity using routine clinical data from English general practices, linked to inpatient hospital, mortality, and socio-economic status data. We studied 3 937 191 adults with median follow-up of 4.2 years (49% men, mean age=39.7 years) between April 1, 2006 and March 31, 2017. Interrupted time series analysis was used to examine the impact of changes to English hypertension guidelines in 2011 on incidence of hypertension (primary outcome). Secondary outcomes included rate of out-of-office monitoring and cardiovascular events. Across the study period, incidence of hypertension fell from 2.1 to 1.4 per 100 person-years. The change in guidance in 2011 was not associated with an immediate change in incidence (change in rate=0.01 [95% CI, -0.18-0.20]) but did result in a leveling out of the downward trend (change in yearly trend =0.09 [95% CI, 0.04-0.15]). Ambulatory monitoring increased significantly in 2011/2012 (change in rate =0.52 [95% CI, 0.43-0.60]). The rate of cardiovascular events remained unchanged (change in rate =-0.02 [95% CI, -0.05-0.02]). In summary, changes to hypertension guidelines in 2011 were associated with a stabilisation in incidence and no increase in cardiovascular events. Guidelines should continue to recommend out-of-office monitoring for diagnosis of hypertension

The association between antihypertensive treatment and serious adverse events by age and frailty: A cohort study

BACKGROUND: Antihypertensives are effective at reducing the risk of cardiovascular disease, but limited data exist quantifying their association with serious adverse events, particularly in older people with frailty. This study aimed to examine this association using nationally representative electronic health record data. METHODS AND FINDINGS: This was a retrospective cohort study utilising linked data from 1,256 general practices across England held within the Clinical Practice Research Datalink between 1998 and 2018. Included patients were aged 40+ years, with a systolic blood pressure reading between 130 and 179 mm Hg, and not previously prescribed antihypertensive treatment. The main exposure was defined as a first prescription of antihypertensive treatment. The primary outcome was hospitalisation or death within 10 years from falls. Secondary outcomes were hypotension, syncope, fractures, acute kidney injury, electrolyte abnormalities, and primary care attendance with gout. The association between treatment and these serious adverse events was examined by Cox regression adjusted for propensity score. This propensity score was generated from a multivariable logistic regression model with patient characteristics, medical history and medication prescriptions as covariates, and new antihypertensive treatment as the outcome. Subgroup analyses were undertaken by age and frailty. Of 3,834,056 patients followed for a median of 7.1 years, 484,187 (12.6%) were prescribed new antihypertensive treatment in the 12 months before the index date (baseline). Antihypertensives were associated with an increased risk of hospitalisation or death from falls (adjusted hazard ratio [aHR] 1.23, 95% confidence interval (CI) 1.21 to 1.26), hypotension (aHR 1.32, 95% CI 1.29 to 1.35), syncope (aHR 1.20, 95% CI 1.17 to 1.22), acute kidney injury (aHR 1.44, 95% CI 1.41 to 1.47), electrolyte abnormalities (aHR 1.45, 95% CI 1.43 to 1.48), and primary care attendance with gout (aHR 1.35, 95% CI 1.32 to 1.37). The absolute risk of serious adverse events with treatment was very low, with 6 fall events per 10,000 patients treated per year. In older patients (80 to 89 years) and those with severe frailty, this absolute risk was increased, with 61 and 84 fall events per 10,000 patients treated per year (respectively). Findings were consistent in sensitivity analyses using different approaches to address confounding and taking into account the competing risk of death. A strength of this analysis is that it provides evidence regarding the association between antihypertensive treatment and serious adverse events, in a population of patients more representative than those enrolled in previous randomised controlled trials. Although treatment effect estimates fell within the 95% CIs of those from such trials, these analyses were observational in nature and so bias from unmeasured confounding cannot be ruled out. CONCLUSIONS: Antihypertensive treatment was associated with serious adverse events. Overall, the absolute risk of this harm was low, with the exception of older patients and those with moderate to severe frailty, where the risks were similar to the likelihood of benefit from treatment. In these populations, physicians may want to consider alternative approaches to management of blood pressure and refrain from prescribing new treatment

Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI

BACKGROUND: Antihypertensives reduce the risk of cardiovascular disease but are also associated with harms including acute kidney injury (AKI). Few data exist to guide clinical decision making regarding these risks. AIM: To develop a prediction model estimating the risk of AKI in people potentially indicated for antihypertensive treatment. DESIGN AND SETTING: Observational cohort study using routine primary care data from the Clinical Practice Research Datalink (CPRD) in England. METHOD: People aged ≥40 years, with at least one blood pressure measurement between 130 mmHg and 179 mmHg were included. Outcomes were admission to hospital or death with AKI within 1, 5, and 10 years. The model was derived with data from CPRD GOLD (n = 1 772 618), using a Fine-Gray competing risks approach, with subsequent recalibration using pseudo-values. External validation used data from CPRD Aurum (n = 3 805 322). RESULTS: The mean age of participants was 59.4 years and 52% were female. The final model consisted of 27 predictors and showed good discrimination at 1, 5, and 10 years (C-statistic for 10-year risk 0.821, 95% confidence interval [CI] = 0.818 to 0.823). There was some overprediction at the highest predicted probabilities (ratio of observed to expected event probability for 10-year risk 0.633, 95% CI = 0.621 to 0.645), affecting patients with the highest risk. Most patients (>95%) had a low 1- to 5-year risk of AKI, and at 10 years only 0.1% of the population had a high AKI and low CVD risk. CONCLUSION: This clinical prediction model enables GPs to accurately identify patients at high risk of AKI, which will aid treatment decisions. As the vast majority of patients were at low risk, such a model may provide useful reassurance that most antihypertensive treatment is safe and appropriate while flagging the few for whom this is not the case

Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI

Background Antihypertensives reduce the risk of cardiovascular disease but are also associated with harms including acute kidney injury (AKI). Few data exist to guide clinical decision making regarding these risks. Aim To develop a prediction model estimating the risk of AKI in people potentially indicated for antihypertensive treatment. Design and setting Observational cohort study using routine primary care data from the Clinical Practice Research Datalink (CPRD) in England. Method People aged ≥40 years, with at least one blood pressure measurement between 130 mmHg and 179 mmHg were included. Outcomes were admission to hospital or death with AKI within 1, 5, and 10 years. The model was derived with data from CPRD GOLD (n = 1 772 618), using a Fine–Gray competing risks approach, with subsequent recalibration using pseudo-values. External validation used data from CPRD Aurum (n = 3 805 322). Results The mean age of participants was 59.4 years and 52% were female. The final model consisted of 27 predictors and showed good discrimination at 1, 5, and 10 years (C-statistic for 10-year risk 0.821, 95% confidence interval [CI] = 0.818 to 0.823). There was some overprediction at the highest predicted probabilities (ratio of observed to expected event probability for 10-year risk 0.633, 95% CI = 0.621 to 0.645), affecting patients with the highest risk. Most patients (>95%) had a low 1- to 5-year risk of AKI, and at 10 years only 0.1% of the population had a high AKI and low CVD risk. Conclusion This clinical prediction model enables GPs to accurately identify patients at high risk of AKI, which will aid treatment decisions. As the vast majority of patients were at low risk, such a model may provide useful reassurance that most antihypertensive treatment is safe and appropriate while flagging the few for whom this is not the case

Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study

BackgroundPatients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis.AimTo generate a clinical prediction rule to identify primary care patients who are at highest risk of myeloma.Design and settingRetrospective open cohort study using electronic health records data from the UK’s Clinical Practice Research Datalink (CPRD) between 1 January 2000 and 1 January 2014.MethodPatients from the CPRD were included in the study if they were aged ≥40 years, had two full blood counts within a year, and had no previous diagnosis of myeloma. Cases of myeloma were identified in the following 2 years. Derivation and external validation datasets were created based on geographical region. Prediction equations were estimated using Cox proportional hazards models including patient characteristics, symptoms, and blood test results. Calibration, discrimination, and clinical utility were evaluated in the validation set.ResultsOf 1 281 926 eligible patients, 737 (0.06%) were diagnosed with myeloma within 2 years. Independent predictors of myeloma included: older age; male sex; back, chest and rib pain; nosebleeds; low haemoglobin, platelets, and white cell count; and raised mean corpuscular volume, calcium, and erythrocyte sedimentation rate. A model including symptoms and full blood count had an area under the curve of 0.84 (95% CI = 0.81 to 0.87) and sensitivity of 62% (95% CI = 55% to 68%) at the highest risk decile. The corresponding statistics for a second model, which also included calcium and inflammatory markers, were an area under the curve of 0.87 (95% CI = 0.84 to 0.90) and sensitivity of 72% (95% CI = 66% to 78%).ConclusionThe implementation of these prediction rules would highlight the possibility of myeloma in patients where GPs do not suspect myeloma. Future research should focus on the prospective evaluation of further external validity and the impact on clinical practice.</jats:sec