146Mendelian randomisation for mediation analysis: current methods and challenges for implementation

Background Mendelian randomisation uses genetic variants randomly allocated at conception as instrumental variables for an exposure. Methodological advances allow for mediation analysis to be carried out using Mendelian randomisation using either multivariable Mendelian randomisation or two-step Mendelian randomisation. Methods We use simulations and an applied example to demonstrate when multivariable Mendelian randomisation and two-step Mendelian randomisation methods are valid and how they relate to traditional phenotypic regression-based approaches to mediation. We demonstrate how Mendelian randomisation methods can relax assumptions required for causal inference in phenotypic mediation, as well as which Mendelian randomisation specific assumptions are required. We illustrate our methods in data from UK Biobank, estimating the role of body mass index mediating the association between education and cardiovascular outcomes. Results Both multivariable Mendelian randomization and two-step Mendelian randomization are unbiased when estimating the total effect, direct effect, indirect effect and proportion mediated when both confounding, and measurement error are present. Multivariable Mendelian Randomization can be used when multiple mediators are to be investigated in a single model. Conclusions Mendelian randomisation provides an opportunity to improve causal inference in mediation analysis. Although Mendelian randomisation specific assumptions apply, such as no weak instrument bias and no pleiotropic pathways, strong phenotypic assumptions of no confounding and no measurement error can be relaxed. Key messages Mendelian randomisation offers an opportunity to address bias by unmeasured confounding, measurement error and reverse causality in mediation analysis.

Navigating sample overlap, winner's curse and weak instrument bias in Mendelian randomization studies using the UK Biobank

We performed GWAS on 2514 complex traits from the UK Biobank using a linear mixed model, identifying 40,620 independent significant associations (p<5x10-8). We estimate that winner's curse incurs substantial overestimation of effect sizes in a mean of 35% of discovered associations per trait. We use these results to estimate that the polygenicity of most complex traits is below 10000 common causal variants. We evaluated the impact of winner's curse on causal effect estimation and hypothesis testing in Mendelian randomization analyses. We show that winner's curse substantially amplifies the magnitude of weak instrument bias, though any inflation of false discovery rates tends to be low or modest. We designed a process of pseudo-replication within the UK Biobank data to generate GWAS estimates that minimise bias in MR studies using these data. Our resource is integrated into the OpenGWAS platform and enables a convenient framework for researchers to minimise bias or maximise precision of causal effect estimates.

A Climate Hyperspectral Infrared Radiance Product (CHIRP) Combining the AIRS and CrIS Satellite Sounding Record

A Climate Hyperspectral Infrared Radiance Product (CHIRP) is introduced combining data from the Atmospheric Infrared Sounder (AIRS) on NASA’s EOS-AQUA platform, the Cross-Track Infrared Sounder (CrIS) sounder on NASA’s SNPP platform, and continuing with CRIS sounders on the NOAA/NASA Joint Polar Satellite Series (JPSS) of polar satellites. The CHIRP product converts the parent instrument’s radiances to a common Spectral Response Function (SRF) and removes inter-satellite biases, providing a consistent inter-satellite radiance record. The CHIRP record starts in September 2002 with AIRS, followed by CrIS SNPP and the JPSS series of CrIS instruments. The CHIRP record should continue until the mid-2040’s as additional JPSS satellites are launched. These sensors, in CHIRP format, provide the climate community with a homogeneous sensor record covering much of the infrared. We give an overview of the conversion of AIRS and CrIS to CHIRP, and define the SRF for common CHIRP format. Considerable attention is paid to removing static bias offsets among these three sensors. The CrIS instrument on NASA’s SNPP satellite is used as the calibration standard. Simultaneous Nadir Overpasses (SNOs) as well as large statistical samplings of radiances from these three satellites are used to derive the instrument bias offsets and estimate the bias offset accuracy, which is ~0.03 K. In addition, possible scene-dependent calibration differences between CHIRP derived from AIRS and CHIRP derived from CrIS on the SNPP platform are presented.

Instrument Bias Correction With Machine Learning Algorithms: Application to Field-Portable Mass Spectrometry

In situ sensors for environmental chemistry promise more thorough observations, which are necessary for high confidence predictions in earth systems science. However, these can be a challenge to interpret because the sensors are strongly influenced by temperature, humidity, pressure, or other secondary environmental conditions that are not of direct interest. We present a comparison of two statistical learning methods—a generalized additive model and a long short-term memory neural network model for bias correction of in situ sensor data. We discuss their performance and tradeoffs when the two bias correction methods are applied to data from submersible and shipboard mass spectrometers. Both instruments measure the most abundant gases dissolved in water and can be used to reconstruct biochemical metabolisms, including those that regulate atmospheric carbon dioxide. Both models demonstrate a high degree of skill at correcting for instrument bias using correlated environmental measurements; the difference in their respective performance is less than 1% in terms of root mean squared error. Overall, the long short-term memory bias correction produced an error of 5% for O2 and 8.5% for CO2 when compared against independent membrane DO and laser spectrometer instruments. This represents a predictive accuracy of 92–95% for both gases. It is apparent that the most important factor in a skillful bias correction is the measurement of the secondary environmental conditions that are likely to correlate with the instrument bias. These statistical learning methods are extremely flexible and permit the inclusion of nearly an infinite number of correlates in finding the best bias correction solution.

Effects of Broad Spectrum Antibiotics on Measurement of Immunosuppressant Drugs

Background: Antibiotics used parenterally can affect blood drug level measurements, as measured in diagnostic tests. Objective: To investigate the effect of six different antibiotics commonly used in intensive care units on tacrolimus, sirolimus, everolimus and cyclosporin A levels measured by mass spectrometry. Methods: Ampicillin + sulbactam (AB1, IV, 1 g), imipenem + cilastatin sodium (AB2, IV, 500 mg), piperacillin + tazobactam (AB3, 4.5 g, IV), ertapenem (AB4, IV, 1 g), meropenem trihydrate (AB5, 500 mg, IV) and ceftriaxone (AB6, 1 g, IV) antibiotics were used for the interference assay. Measurements were performed on the Shimadzu 8045 (Japan) LC-MS/MS instrument. Bias values were calculated. Results: The least affected immunosuppressant was cyclosporine A (between -6.88% and 3.40%). The most affected were everolimus and sirolimus. Ertapenem caused negative interference on the level of everolimus at the rate of -27.34% and sirolimus at the rate of -26.79%. Piperacillin + tazobactam and imipenem + cilastatin sodium caused positive interferences on sirolimus at the rate of 24.24% and 22.73%, respectively. Ampicillin + sulbactam, meropenem trihydrate and ceftriaxone affected the sirolimus levels at lower rates (-4.49%, 5.93% and 9.86%). Everolimus levels deviated at the rate of -11.21% to -16.99% due to imipenem + cilastatin sodium, meropenem trihydrate and ceftriaxone. Conclusion: This study demonstrated the potential of antibiotic use affecting immunosuppressant levels. Antibiotic interference, especially in transplant patients, may cause erroneous immunosuppression, increasing the likelihood of rejection.

Fast 0/1-h algorithm for detection of NSTEMI: are current high-sensitivity cardiac troponin assays fit for purpose? An EQA-based evaluation

Background High-sensitivity cardiac troponin T/I (hs-cTnT/I) assays have improved analytical sensitivity for the detection of myocardial infarction (MI). To gain clinical specificity and sensitivity, interpretation of changes in cTn concentrations over time is crucial. The 2015 ESC NSTEMI guideline defines absolute delta values as additional rule-in and rule-out criteria for MI. A critical assumption for application of this rule is that total analytical imprecision within the delta period, including inter-instrument bias, is comparable to analytical imprecision in the validation studies. Methods Data from the Dutch External Quality Assessment Scheme (EQAS) were used to calculate inter-instrument bias and estimate imprecision for the measuring range where the proposed delta values are relevant: for Roche Elecsys hs-cTnT, 5–52 and 5–12 ng/L; for Abbott Architect hs-cTnI, 2–52 and 2–5 ng/L for rule-in and rule-out, respectively. Results For Elecsys, the median inter-instrument bias is 0.3 ng/L (n = 33 laboratories), resulting in reference change values (RCVs) of 3.0 and 1.7 ng/L, respectively, for rule-in and rule-out with imprecision as claimed by the manufacturer. With RCVs smaller than the guideline’s delta thresholds, 100% of the laboratories have adequate specifications. RCVs for rule-in/rule-out increased to 4.6 ng/L/2.5 ng/L, respectively, with individual imprecisions as estimated from EQA data, resulting in 64% and 82% of laboratories with adequate specifications. For Architect, 40% of instruments (n = 10) might falsely qualify the result as clinically relevant; hence, inter-instrument bias could not be determined. Conclusions We advise laboratories that use the fast 0/1-h algorithm to introduce stringent internal quality procedures at the relevant/low concentration level, especially when multiple analyzers are randomly used.

False Immunosuppressant Measurement by LC-MS/MS Method Due to Radiopaque Agents

Background: Although liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is preferred as a reliable method, some molecules in the blood matrix may lead to false positive or false negative results. False positive or negative results show the direction of the deviation rate from the target value. Aim: The aim of this study was to investigate the effect of seven different radiopaque agents on four immunosuppressant drugs. Methods: Every agent coded with RM1 to RM7 was added to control materials containing tacrolimus, everolimus, sirolimus, and cyclosporine A drugs. Measurements were performed using an LC-MS/MS instrument. Bias values were calculated to detect the deviation rates. Results: All RMs led to false negative results in the tacrolimus and cyclosporine A levels at a rate of -19.77% (95% CI, -27.16 to 12.52) to -44.45% (95% CI, -49.20 to -39.69). The smallest deviations were seen in the everolimus levels with the administration of RM6 (gadodiamide) and in the sirolimus levels with RM1 (gadobutrol) at the rates of 4.04% (95% CI, -11.36 to -3.17) and 2.11% (95% CI, -7.18 to 7.11), respectively. The most affected drug by RM4 (gadopentetate dimeglumine salt) was sirolimus at the rate of 114.01% (95% CI, 97.31 - 130.76). RM5 (gadodiamide) interfered cyclosporine A at the most. The highest deviations were observed with the administration of RM3 (iohexol) in the everolimus and sirolimus levels at the rates of 153.72% (95% CI, 142.44 to 164.78) and 171.41% (95% CI, 157.91 to 184.97), respectively. Conclusion: Radiopaque agents interfered the measurement of immunosuppressant drugs. Especially, everolimus and sirolimus levels were affected due to using iohexol. The choice of gadodiamide or ioversol is important to reduce the risk of interference for everolimus measurement. The blood samples should be obtained for measurement of drug levels before contrast-enhanced imaging.

Extending causality tests with genetic instruments: an integration of Mendelian Randomization and the Classical Twin Design

ABSTRACTMendelian Randomization (MR) is an important approach to modelling causality in non-experimental settings. MR uses genetic instruments to test causal relationships between exposures and outcomes of interest. Individual genetic variants have small effects, and so, when used as instruments, render MR liable to weak instrument bias. Polygenic scores have the advantage of larger effects, but may be characterized by direct pleiotropy, which violates a central assumption of MR.We developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model. This model allows us to test pleiotropy directly. We considered the issue of parameter identification, and given identification, we conducted extensive power calculations. MR-DoC allows one to test causal hypotheses and to obtain unbiased estimates of the causal effect given pleiotropic instruments (polygenic scores), while controlling for genetic and environmental influences common to the outcome and exposure. Furthermore, MR-DoC in twins has appreciably greater statistical power than a standard MR analysis applied to singletons, if the unshared environmental effects on the exposure and the outcome are uncorrelated. Generally, power increases with: 1) decreasing residual exposure-outcome correlation, and 2) decreasing heritability of the exposure variable.MR-DoC allows one to employ strong instrumental variables (polygenic scores, possibly pleiotropic), guarding against weak instrument bias and increasing the power to detect causal effects. Our approach will enhance and extend MR’s range of applications, and increase the value of the large cohorts collected at twin registries as they correctly detect causation and estimate effect sizes even in the presence of pleiotropy.

An assessment of two automated snow water equivalent instruments during the WMO Solid Precipitation Intercomparison Experiment

During the World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE), automated measurements of snow water equivalent (SWE) were made at the Sodankylä (Finland), Weissfluhjoch (Switzerland) and Caribou Creek (Canada) SPICE sites during the northern hemispheric winters of 2013/14 and 2014/15. Supplementary intercomparison measurements were made at Fortress Mountain (Kananaskis, Canada) during the 2013/14 winter. The objectives of this analysis are to compare automated SWE measurements with a reference, comment on their performance and, where possible, to make recommendations on how to best use the instruments and interpret their measurements. Sodankylä, Caribou Creek and Fortress Mountain hosted a Campbell Scientific CS725 passive gamma radiation SWE sensor. Sodankylä and Weissfluhjoch hosted a Sommer Messtechnik SSG1000 snow scale. The CS725 operating principle is based on measuring the attenuation of soil emitted gamma radiation by the snowpack and relating the attenuation to SWE. The SSG1000 measures the mass of the overlying snowpack directly by using a weighing platform and load cell. Manual SWE measurements were obtained at the intercomparison sites on a bi-weekly basis over the accumulation–ablation periods using bulk density samplers. These manual measurements are considered to be the reference for the intercomparison. Results from Sodankylä and Caribou Creek showed that the CS725 generally overestimates SWE as compared to manual measurements by roughly 30–35 % with correlations (r2) as high as 0.99 for Sodankylä and 0.90 for Caribou Creek. The RMSE varied from 30 to 43 mm water equivalent (mm w.e.) and from 18 to 25 mm w.e. at Sodankylä and Caribou Creek, which had respective SWE maximums of approximately 200 and 120 mm w.e. The correlation at Fortress Mountain was 0.94 (RMSE of 48 mm w.e. with a maximum SWE of approximately 650 mm w.e.) with no systematic overestimation. The SSG1000 snow scale, having a different measurement principle, agreed quite closely with the manual measurements at Sodankylä and Weissfluhjoch throughout the periods when data were available (r2 as high as 0.99 and RMSE from 8 to 24 mm w.e. at Sodankylä and from 56 to 59 mm w.e. at Weissfluhjoch, where maximum SWE was approximately 850 mm w.e.). When the SSG1000 was compared to the CS725 at Sodankylä, the agreement was linear until the start of ablation when the positive bias in the CS725 increases substantially relative to the SSG1000. Since both Caribou Creek and Sodankylä have sandy soil, water from the snowpack readily infiltrates into the soil during melt, even if the soil is frozen. However, the CS725 does not differentiate this water from the unmelted snow. This issue can be identified, at least during the late spring ablation period, with soil moisture and temperature observations like those measured at Caribou Creek. With a less permeable soil and surface runoff, the increase in the instrument bias during ablation is not as significant, as shown by the Fortress Mountain intercomparison.