Effect of mild cortisol cosecretion on body composition and metabolic parameters in patients with primary hyperaldosteronism

Objective To investigate the effects of simultaneous cortisol cosecretion (CCS) on body composition in computed tomography (CT)-imaging and metabolic parameters in patients with primary aldosteronism (PA) with the objective of facilitating early detection. Design Retrospective cohort study. Patients Forty-seven patients with PA and CCS confirmed by 1-mg dexamethasone suppression test (DST) with a cutoff of ≥1.8 µg/dL were compared with PA patients with excluded CCS (non-CCS, n = 47) matched by age and sex. Methods Segmentation of the fat compartments and muscle area at the third lumbar region was performed on non-contrast-enhanced CT images with dedicated segmentation software. Additionally, liver, spleen, pancreas and muscle attenuation were compared between the two groups. Results Mean cortisol after DST was 1.2 µg/dL (33.1 nmol/L) in the non-CCS group and 3.2 µg/dL (88.3 nmol/L) in the CCS group with mild autonomous cortisol excess (MACE). No difference in total, visceral and subcutaneous fat volumes was observed between the CCS and non-CCS group (p = .7, .6 and .8, respectively). However, a multivariable regression analysis revealed a significant correlation between total serum cholesterol and results of serum cortisol after 1-mg DST (p = .026). Classification of the patients based on visible lesion on CT and PA-lateralization via adrenal venous sampling also did not show any significant differences in body composition. Conclusion MACE in PA patients does not translate into body composition changes on CT-imaging. Therefore, early detection of concurrent CCS in PA is currently only attainable through biochemical tests. Further investigation of the long-term clinical adverse effects of MACE in PA is necessary

Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism

ObjectivesThe aim of this study was to investigate an integrated diagnostics approach for prediction of the source of aldosterone overproduction in primary hyperaldosteronism (PA).Methods269 patients from the prospective German Conn Registry with PA were included in this study. After segmentation of adrenal glands in native CT images, radiomic features were calculated. The study population consisted of a training (n = 215) and a validation (n = 54) cohort. The k = 25 best radiomic features, selected using maximum-relevance minimum-redundancy (MRMR) feature selection, were used to train a baseline random forest model to predict the result of AVS from imaging alone. In a second step, clinical parameters were integrated. Model performance was assessed via area under the receiver operating characteristic curve (ROC AUC). Permutation feature importance was used to assess the predictive value of selected features.ResultsRadiomics features alone allowed only for moderate discrimination of the location of aldosterone overproduction with a ROC AUC of 0.57 for unilateral left (UL), 0.61 for unilateral right (UR), and 0.50 for bilateral (BI) aldosterone overproduction (total 0.56, 95% CI: 0.45-0.65). Integration of clinical parameters into the model substantially improved ROC AUC values (0.61 UL, 0.68 UR, and 0.73 for BI, total 0.67, 95% CI: 0.57-0.77). According to permutation feature importance, lowest potassium value at baseline and saline infusion test (SIT) were the two most important features.ConclusionIntegration of clinical parameters into a radiomics machine learning model improves prediction of the source of aldosterone overproduction and subtyping in patients with PA

The ability of the adjoint technique to recover decadal variability of the North Atlantic circulation

Different oceanic data assimilation products show rather different decadal-scale variability, in particular for the Atlantic meridional overturning circulation (MOC). In order to understand these differences we evaluate the ability of the adjoint technique to reproduce MOC variability using surface heat flux forcing as the control parameter. We find that in a perfect model framework and for a reasonable weighting the adjoint method is, in principle, successful at reproducing decadal-scale MOC variability if adequate synthetic observations and a priori information of the control parameter are given. Temperature of the upper 1000 m and sea surface height and a priori information about surface heat fluxes contain the most useful information. Using only salinity or only synthetic hydrography below 1000 m, the method fails to converge and to reconstruct MOC variability, given surface heat flux as the only control parameter. In order to provide error bounds for current assimilation products, prescribed artificial errors for a priori control parameter, synthetic observations and initial conditions are introduced systematically to our setup. We find that errors with reasonable magnitude in synthetic observations as well as a priori information of the surface heat fluxes lead to a reconstructed decadal-scale MOC variability with tolerable errors of less than a few percent. Errors in initial conditions lead to a "cold start" problem and can degrade the quality of the MOC reconstruction, but can be damped by sufficient a priori information about the surface forcing in the subsequent integration, even without including the initial conditions as a control parameter. The impact of a model error is analyzed by assimilating synthetic observations from different model configurations, which resembles most likely an underestimation of the "real" model error. Even with this optimistic estimate, the reconstruction is very sensitive to the model error and leads to a large error in the reconstructed MOC variability. Taking all possible errors together, the error of decadal MOC reconstruction in current data assimilation products appears to be larger than 60% (about 1 Sv) with a correlation with the "real" MOC variability by less than 0.5. (C) 2013 Elsevier Ltd. All rights reserved

Convective and Diffusive Energetic Particle Losses Induced by Shear Alfven Waves in the ASDEX Upgrade Tokamak

We present here the first phase-space characterization of convective and diffusive energetic particle losses induced by shear Alfven waves in a magnetically confined fusion plasma. While single toroidal Alfven eigenmodes (TAE) and Alfven cascades (AC) eject resonant fast ions in a convective process, an overlapping of AC and TAE spatial structures leads to a large fast-ion diffusion and loss. Diffusive fast-ion losses have been observed with a single TAE above a certain threshold in the fluctuation amplitude

Fast-ion losses induced by ACs and TAEs in the ASDEX Upgrade tokamak

The phase-space of convective and diffusive fast-ion losses induced by shear Alfven eigenmodes has been characterized in the ASDEX Upgrade tokamak. Time-resolved energy and pitch-angle measurements of fast-ion losses correlated in frequency and phase with toroidal Alfven eigenmodes (TAEs) and Alfven cascades (ACs) have allowed to identify both loss mechanisms. While single ACs and TAEs eject resonant fast-ions in a convective process, the overlapping of AC and TAE spatial structures leads to a large fast-ion diffusion and loss. The threshold for diffusive fast-ion losses depends on the ion energy (gyroradius). Diffusive fast-ion losses with gyroradius approximate to 70 mm have been observed with a single TAE for local radial displacements of the magnetic field lines larger than approximate to 2 mm. Multiple frequency chirping ACs cause an enhancement of the diffusive losses. The ACs and TAEs radial structures have been reconstructed by means of cross-correlation techniques between the fast-ion loss detector and the electron cyclotron emission radiometer