Citations

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract Background: Laboratory protocols used to calibrate commercial clinical chemistry systems affect test result quality. Mathematical models of system uncertainty can be developed using performance parameters provided by the manufacturer for various subsystems. These models can be used to evaluate protocols for specific laboratory operations. Methods: A mathematical model was developed to estimate the uncertainty inherent in the Roche Diagnostics P-Modular system, and included uncertainties associated with the sample and reagent pipettes, spectrometer and the calibration process. The model was then used to evaluate various alternate calibration protocols: calibration based on mean of replicate measurements (n=1–6) and calibration based on conditional acceptance when the following quality control specimen was within one standard deviation of target. The effect of calibrator concentrations on assay measurement uncertainty was also studied, and calibrator concentrations that minimize uncertainty at a specific concentration were identified. Results: The simulation model produced uncertainty estimates of 3.5% for the serum cholesterol assay and identified sample pipette (40%) and spectrometer (21%) as the largest contributors to measurement uncertainty. Each additional replicate calibrator measurements result in diminishing reductions in measurement uncertainty, with maximum reductions (19%) achieved with five replicate measurements. The conditional acceptance of calibration only when the control was within 1s resulted in an 18% reduction. Conclusions: The model can be used to evaluate the utility of laboratory protocols and establish realistic assay performance targets. The model also can help instrument manufacturers and laboratorians identify major contributors to assay measurement uncertainty, which helps improve performance in future assay systems.

Rationale: The link between obesity, hyperleptinemia, and development of cardiovascular disease is not completely understood. Increases in leptin have been shown to impair leptin signaling via caveolin-1–dependent mechanisms. However, the role of hyperleptinemia versus impaired leptin signaling in adipose tissue is not known. Objective: To determine the presence and significance of leptin-dependent increases in adipose tissue caveolin-1 expression in humans. Methods and Results: We designed a longitudinal study to investigate the effects of increases in leptin on adipose tissue caveolin-1 expression during weight gain in humans. Ten volunteers underwent 8 weeks of overfeeding, during which they gained an average weight of 4.1±1.4 kg, with leptin increases from 7±3.8 to 12±5.7 ng/mL. Weight gain also resulted in changes in adipose tissue caveolin-1 expression, which correlated with increases in leptin (rho=0.79, P =0.01). In cultured human white preadipocytes, leptin increased caveolin-1 expression, which in turn impaired leptin cellular signaling. Functionally, leptin decreased lipid accumulation in differentiating human white preadipocytes, which was prevented by caveolin-1 overexpression. Further, leptin decreased perilipin and fatty acid synthase expression, which play an important role in lipid storage and biogenesis. Conclusions: In healthy humans, increases in leptin, as seen with modest weight gain, may increase caveolin-1 expression in adipose tissue. Increased caveolin-1 expression in turn impairs leptin signaling and attenuates leptin-dependent lowering of intracellular lipid accumulation. Our study suggests a leptin-dependent feedback mechanism that may be essential to facilitate adipocyte lipid storage during weight gain.

AbstractCellular and interpatient heterogeneity and the involvement of different stem and progenitor compartments in leukemogenesis are challenges for the identification of common pathways contributing to the initiation and maintenance of acute myeloid leukemia (AML). Here we used a strategy of parallel transcriptional analysis of phenotypic long-term hematopoietic stem cells (HSCs), short-term HSCs, and granulocyte-monocyte progenitors from individuals with high-risk (−7/7q−) AML and compared them with the corresponding cell populations from healthy controls. This analysis revealed dysregulated expression of 11 genes, including IL-1 receptor accessory protein (IL1RAP), in all leukemic stem and progenitor cell compartments. IL1RAP protein was found to be overexpressed on the surface of HSCs of AML patients, and marked cells with the −7/7q− anomaly. IL1RAP was also overexpressed on HSCs of patients with normal karyotype AML and high-risk myelodysplastic syndrome, suggesting a pervasive role in different disease subtypes. High IL1RAP expression was independently associated with poor overall survival in 3 independent cohorts of AML patients (P = 2.2 × 10−7). Knockdown of IL1RAP decreased clonogenicity and increased cell death of AML cells. Our study identified genes dysregulated in stem and progenitor cells in −7/7q− AML, and suggests that IL1RAP may be a promising therapeutic and prognostic target in AML and high-risk myelodysplastic syndrome.

SummaryNeural‐specific autoantibodies have been documented and their diagnostic utility validated in diseases affecting the neuraxis from cerebral cortex to the somatic, autonomic, and enteric nervous system and skeletal muscle. These neurological disorders occur both idiopathically and in a paraneoplastic context. Molecular identification of the antigens has expedited development of confirmatory and high‐throughput tests for serum and cerebrospinal fluid, which permit early diagnosis and reveal the underlying molecular pathogenic mechanisms. The autoantibodies are classifiable on the basis of antigen location: intracellular (nuclear or cytoplasmic) or plasma membrane. Immunohistopathological studies of patients’ biopsied and autopsied tissues suggest that effector T cells mediate the autoimmune neurological disorders for which defining autoantibodies recognize intracellular antigens. Antigens within intact cells are inaccessible to circulating antibody, and the associated neurological deficits rarely improve with antibody‐depleting therapies. Tumoricidal therapies may arrest neurological progression, but symptom reversal is rare. In contrast, autoantibodies specific for plasma membrane antigens have pathogenic potential, and the associated neurological deficits are often amenable to antibody‐depleting immunotherapy, such as plasma exchange and anti‐B‐cell monoclonal antibody therapy. These reversible neurological disorders are frequently misdiagnosed as neurodegenerative. The focus of this review is the immunobiology, pathophysiology, and clinical spectrum of autoimmune neurological disorders accompanied by neural‐specific IgGs.