Citations

ObjectiveIt is unclear why despite a comparable cardiometabolic risk profile, “metabolically benign” overweight/obese individuals show an elevated risk of cardiovascular disease compared to normal weight individuals.Design and MethodsIn cross‐sectional analyses, we compared levels of ectopic fat (epicardial, pericardial, and hepatic fat) and adipokines (leptin, soluble leptin receptor, and high molecular weight [HMW] adiponectin) among metabolically benign (MBO) and at‐risk overweight/obese (ARO), and metabolically benign normal weight (MBNW) women, screened for the Kronos Early Estrogen Prevention Study. We defined “metabolically benign” with ≤ 1, and “at‐risk” with ≥2 components of the metabolic syndrome.ResultsCompared to MBO women, ARO women had significantly elevated odds of being in the top tertile of epicardial fat (OR: 1.76, 95% CI: 1.04‐2.99), hepatic fat (OR: 1.90, 95% CI:1.12‐3.24) and leptin (OR: 2.15, 95% CI: 1.23‐3.76), and the bottom tertile of HMW‐adiponectin (OR: 2.90, 95% CI: 1.62‐5.19). Compared to MBNW women, MBO women had significantly higher odds of being in the top tertile of epicardial fat (OR: 5.17, 95% CI: 3.22‐8.29), pericardial fat (OR: 9.27, 95% CI: 5.52‐15.56) and hepatic fat (OR: 2.72, 95% CI: 1.77‐4.19) and the bottom tertile of HMW adiponectin levels (OR: 2.51, 95% CI: 1.60‐3.94).ConclusionsLevels of ectopic fat and the adverse adipokine profile increase on a continuum of BMI, suggesting that the metabolically benign phenotype may be a transient state.

AbstractWe previously reported an increased risk of monoclonal gammopathy of undetermined significance (MGUS) in first‐degree relatives of MGUS and multiple myeloma patients. Here, we examine whether primary cytogenetic categories of myeloma differ between patients with and without a family history of MGUS or myeloma. We studied 201 myeloma patients with available data on family history and molecular cytogenetic classification. Myeloma with trisomies was more common in probands who had an affected first‐degree relative with MGUS or myeloma compared with those without a family history (46.9% vs. 33.5%, P = 0.125); however, the difference was not statistically significant. Additional studies on the cytogenetic types of myeloma associated with familial tendency are needed.

AbstractElectrical stimulation of the brain has a 2000 year history. Deep brain stimulation (DBS), one form of neurostimulation, is a functional neurosurgical approach in which a high‐frequency electrical current stimulates targeted brain structures for therapeutic benefit. It is an effective treatment for certain neuropathologic movement disorders and an emerging therapy for psychiatric conditions and epilepsy. Its translational journey did not follow the typical bench‐to‐bedside path, but rather reversed the process. The shift from ancient and medieval folkloric remedy to accepted medical practice began with independent discoveries about electricity during the 19th century and was fostered by technological advances of the 20th. In this paper, we review that journey and discuss how the quest to expand its applications and improve outcomes is taking DBS from the bedside back to the bench.

Stimulated by a recent controversy regarding pressure drops predicted in a giant aneurysm with a proximal stenosis, the present study sought to assess variability in the prediction of pressures and flow by a wide variety of research groups. In phase I, lumen geometry, flow rates, and fluid properties were specified, leaving each research group to choose their solver, discretization, and solution strategies. Variability was assessed by having each group interpolate their results onto a standardized mesh and centerline. For phase II, a physical model of the geometry was constructed, from which pressure and flow rates were measured. Groups repeated their simulations using a geometry reconstructed from a micro-computed tomography (CT) scan of the physical model with the measured flow rates and fluid properties. Phase I results from 25 groups demonstrated remarkable consistency in the pressure patterns, with the majority predicting peak systolic pressure drops within 8% of each other. Aneurysm sac flow patterns were more variable with only a few groups reporting peak systolic flow instabilities owing to their use of high temporal resolutions. Variability for phase II was comparable, and the median predicted pressure drops were within a few millimeters of mercury of the measured values but only after accounting for submillimeter errors in the reconstruction of the life-sized flow model from micro-CT. In summary, pressure can be predicted with consistency by CFD across a wide range of solvers and solution strategies, but this may not hold true for specific flow patterns or derived quantities. Future challenges are needed and should focus on hemodynamic quantities thought to be of clinical interest.