Plasma proteomics for prediction of subclinical coronary artery calcifications in primary prevention.

BACKGROUND AND AIMS: Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors. METHODS: Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS. RESULTS: The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors. CONCLUSIONS: A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.

Large-scale plasma proteomics in the UK Biobank modestly improves prediction of major cardiovascular events in a population without previous cardiovascular disease.

AIMS: Improved identification of individuals at high risk of developing cardiovascular disease would enable targeted interventions and potentially lead to reductions in mortality and morbidity. Our aim was to determine whether use of large-scale proteomics improves prediction of cardiovascular events beyond traditional risk factors (TRFs). METHODS: Using proximity extension assays, 2919 plasma proteins were measured in 38 380 participants of the UK Biobank. Both data- and literature-based feature selection and trained models using extreme gradient boosting machine learning were used to predict risk of major cardiovascular events (MACE: fatal and non-fatal myocardial infarction, stroke and coronary artery revascularisation) during a 10-year follow-up. Area under the curve (AUC) and net reclassification index (NRI) were used to evaluate the additive value of selected protein panels to MACE prediction by Systematic COronary Risk Evaluation 2 (SCORE2) or the 10 TRFs used in SCORE2. RESULTS: SCORE2 and SCORE2 refitted to UK Biobank data predicted MACE with AUCs of 0.740 and 0.749, respectively. data-driven selection identified 114 proteins of greatest relevance for prediction. Prediction of MACE was not improved by using these proteins alone (AUC of 0.758) but was significantly improved by combining these proteins with SCORE2 or the 10 TRFs (AUC=0.771, p<001, NRI=0.140, and AUC=0.767, p=0.03, NRI 0.053, respectively). Literature-based protein selection (113 proteins from five previous studies) also improved risk prediction beyond TRFs while a random selection of 114 proteins did not. CONCLUSIONS: Large-scale plasma proteomics with data-driven and literature-based protein selection modestly improves prediction of future MACE beyond TRFs.

The risk of having a myocardial infarction or stroke is usually assessed by clinical scores including traditional risk factors for cardiovascular disease. The development of new technologies enables the rapid measurement of an increasing number of blood proteins. In this study, we applied machine learning techniques in a UK-based cohort of 38 380 participants with 2919 blood proteins measured. We obtained a set of 114 proteins which improved the prediction of the 10-year risk of major cardiovascular event when added to traditional risk factors. Improvements were also achieved using a set of 113 proteins found in previous studies. However, the magnitude of these improvements was relatively low and the clinical utility of combining these proteins with traditional risk factors in primary prevention will have to be further investigated.

Precipitation thresholds and drought-induced tree die-off: insights from patterns of Pinus edulis mortality along an environmental stress gradient.

Recent regional tree die-off events appear to have been triggered by a combination of drought and heat - referred to as 'global-change-type drought'. To complement experiments focused on resolving mechanisms of drought-induced tree mortality, an evaluation of how patterns of tree die-off relate to highly spatially variable precipitation is needed. Here, we explore precipitation relationships with a die-off event of pinyon pine (Pinus edulis Engelm.) in southwestern North America during the 2002-2003 global-change-type drought. Pinyon die-off and its relationship with precipitation was quantified spatially along a precipitation gradient in north-central New Mexico with standard field plot measurements of die-off combined with canopy cover derived from normalized burn ratio (NBR) from Landsat imagery. Pinyon die-off patterns revealed threshold responses to precipitation (cumulative 2002-2003) and vapor pressure deficit (VPD), with little to no mortality (< 10%) above 600 mm and below warm season VPD of c. 1.7 kPa. [Correction added after online publication 17 June 2013; in the preceding sentence, the word 'below' has been inserted.] Our results refine how precipitation patterns within a region influence pinyon die-off, revealing a precipitation and VPD threshold for tree mortality and its uncertainty band where other factors probably come into play - a response type that influences stand demography and landscape heterogeneity and is of general interest, yet has not been documented.

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources.

Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals' emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

Relationship between norepinephrine dose, tachycardia and outcome in septic shock: A multicentre evaluation.

PURPOSE: Septic shock is associated with massive release of endogenous catecholamines. Adrenergic agents may exacerbate catecholamine toxicity and contribute to poor outcomes. We sought to determine whether an association existed between tachycardia and mortality in septic shock patients requiring norepinephrine for more than 6 h despite adequate volume resuscitation. MATERIALS AND METHODS: Multicentre retrospective observational study on 730 adult patients in septic shock consecutively admitted to eight European ICUs between 2011 and 2013. Three timepoints were selected: T1 (first hour of infusion of norepinephrine), Tpeak (time of highest dose during the first 24 h of treatment), and T24 (24-h post-T1). Binary logistic regression models were constructed for the three time-points. RESULTS: Overall ICU mortality was 38.4%. Mortality was higher in those requiring high-dose (≥0.3 mcg/kg/min) versus low-dose (<0.3 mcg/kg/min) norepinephrine at T1 (53.4% vs 30.6%; p < 0.001) and T24 (61.4% vs 20.4%; p < 0.0001). Patients requiring high-dose with concurrent tachycardia had higher mortality at T1; in the low-dose group tachycardia was not associated with mortality. Resolving tachycardia (from T1 to T24) was associated with lower mortality compared to patients where tachycardia persisted (27.8% vs 46.4%; p = 0.001). CONCLUSIONS: Use of high-dose norepinephrine and concurrent tachycardia are associated with poor outcomes in septic shock.

Influence of intra-abdominal pressure on the specificity of pulse pressure variations to predict fluid responsiveness.

BACKGROUND: The positive predictive value of pulse pressure variations (ΔPP) to discriminate patients who should respond to volume expansion (VE) may be altered in mechanically ventilated patients. Our goal was to determine whether intra-abdominal pressure (IAP) measurements could discriminate patients with true-positive ΔPP values versus patients with false-positive ΔPP values. METHODS: We designed a prospective pathophysiologic study in a mixed intensive care unit of a university hospital. Sixteen mechanically ventilated patients with hypotension (SAP, <90 mm Hg) and with ΔPP of 13% or more were included. Cardiac output was assessed using Doppler echocardiography before and after VE; IAP was measured using the bladder pressure method. Patients were classified into two groups according to their response to a standardized VE (500 mL of NaCl 0.9%): responders (≥15% increase in cardiac output) and nonresponders. RESULTS: Nine patients (57%) were responders, and seven patients (43%) were nonresponders. Before VE, IAP was statistically higher in nonresponders (15 [11-22] mm Hg vs. 9 [6.5-11] mm Hg; p = 0.008). The area under the curve of the receiver operating characteristic curve was 0.9 ± 0.08. In patients with ΔPP of 13% or more, an IAP cutoff value of 10.5 mm Hg discriminated between responders and nonresponders with a sensitivity of 100% (59-100%) and a specificity of 78% (40-97%). CONCLUSION: An increase in IAP of more than 10.5 mm Hg can decrease the positive predictive value of ΔPP. Hence, in patients prone to present abnormal IAP values, IAP should be measured before performing VE directed by the ΔPP marker. LEVEL OF EVIDENCE: Diagnostic study, level II.