Search

Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates the patients' molecular profiles with the patients' survival. With complex relationships between survival and high‐dimensional molecular predictors, it is challenging to conduct nonparametric modeling and irrelevant predictors removing simultaneously. In this article, we build a kernel Cox proportional hazards semi‐parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and nonparametric predictors through a LASSO penalty. An efficient high‐dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict the patients' death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes. [ABSTRACT FROM AUTHOR]

Clinical studies examining the effectiveness of a treatment with respect to some primary outcome often require long‐term follow‐up of patients and/or costly or burdensome measurements of the primary outcome of interest. Identifying a surrogate marker for the primary outcome of interest may allow one to evaluate a treatment effect with less follow‐up time, less cost, or less burden. While much clinical and statistical work has focused on identifying and validating surrogate markers, available approaches tend to focus on settings in which only a single surrogate marker is of interest. Limited work has been done to accommodate the high‐dimensional surrogate marker setting where the number of potential surrogates is greater than the sample size. In this article, we develop methods to estimate the proportion of treatment effect explained by high‐dimensional surrogates. We study the asymptotic properties of our proposed estimator, propose inference procedures, and examine finite sample performance via a simulation study. We illustrate our proposed methods using data from a randomized study comparing a novel whey‐based oral nutrition supplement with a standard supplement with respect to change in body fat percentage over 12 weeks, where the surrogate markers of interest are gene expression probesets. [ABSTRACT FROM AUTHOR]

Atherosclerosis is the main cause of cardiovascular diseases. The Fat‐1 gene can express the n‐3 fatty acid desaturase, which converts n‐6 polyunsaturated fatty acids (PUFA) to n‐3 PUFAs. The role of n‐3 PUFAs in atherosclerosis is widely debated. This study explored the effect of n‐3 PUFAs on atherosclerosis in rabbits. In this study, atherosclerosis was induced in Fat‐1 transgenic rabbits and their littermate (WT) rabbits by feeding a high‐cholesterol diet containing 0.3% cholesterol and 3% soybean oil for 16 weeks. Plasma lipid, fatty acid and pathological analyses of atherosclerotic lesions were conducted. Fatty acid composition in the liver and muscle showed that n‐3 PUFAs increased and n‐6 PUFAs decreased in the Fat‐1 group. Plasma high‐density lipoprotein cholesterol (HDL‐C) levels were significantly increased in the Fat‐1 group, and the atherosclerotic lesion area of the aortic arch in Fat‐1 transgenic rabbits was significantly reduced. Histological analysis showed that smooth muscle cells (SMCs) in atherosclerotic lesions decreased significantly. In conclusion, n‐3 PUFAs improve atherosclerosis in Fat‐1 transgenic rabbits, and this process may depend on the increase in plasma HDL‐C and the decrease in the amount of SMCs in atherosclerotic plaques. [ABSTRACT FROM AUTHOR]

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad‐spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two‐center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine‐learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine‐learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30‐day mortality, and 3‐day inpatient re‐admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high‐risk groups showed significant differences in LOS (p < 0.0001), 30‐day mortality (p < 0.0001), and 30‐day inpatient readmission (p < 0.0001). In conclusion, a machine‐learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture. [ABSTRACT FROM AUTHOR]

Background Although diabetes attenuates abdominal aortic aneurysms (AAAs), the mechanisms by which diabetes suppresses AAAs remain incompletely understood. Accumulation of advanced glycation end- (AGEs) reduces extracellular matrix (ECM) degradation in diabetes. Because ECM degradation is critical for AAA pathogenesis, we investigated whether AGEs mediate experimental AAA suppression in diabetes by blocking AGE formation or disrupting AGE-ECM cross-linking using small molecule inhibitors. Methods and Results Male C57BL/6J mice were treated with streptozotocin and intra-aortic elastase infusion to induce diabetes and experimental AAAs, respectively. Aminoguanidine (AGE formation inhibitor, 200 mg/kg), alagebrium (AGE-ECM cross-linking disrupter, 20 mg/kg), or vehicle was administered daily to mice from the last day following streptozotocin injection. AAAs were assessed via serial aortic diameter measurements, histopathology, and in vitro medial elastolysis assays. Treatment with aminoguanidine, not alagebrium, diminished AGEs in diabetic AAAs. Treatment with both inhibitors enhanced aortic enlargement in diabetic mice as compared with vehicle treatment. Neither enhanced AAA enlargement in nondiabetic mice. AAA enhancement in diabetic mice by aminoguanidine or alagebrium treatment promoted elastin degradation, smooth muscle cell depletion, mural macrophage accumulation, and neoangiogenesis without affecting matrix metalloproteinases, C-C motif chemokine ligand 2, or serum glucose concentration. Additionally, treatment with both inhibitors reversed suppression of diabetic aortic medial elastolysis by porcine pancreatic elastase in vitro. Conclusions Inhibiting AGE formation or AGE-ECM cross-linking enhances experimental AAAs in diabetes. These findings support the hypothesis that AGEs attenuate experimental AAAs in diabetes. These findings underscore the potential translational value of enhanced ECM cross-linking as an inhibitory strategy for early AAA disease.

Background Interleukin-19 is an immunosuppressive cytokine produced by immune and nonimmune cells, but its role in abdominal aortic aneurysm (AAA) pathogenesis is not known. This study aimed to investigate interleukin-19 expression in, and influences on, the formation and progression of experimental AAAs. Methods and Results Human specimens were obtained at aneurysm repair surgery or from transplant donors. Experimental AAAs were created in 10- to 12-week-old male mice via intra-aortic elastase infusion. Influence and potential mechanisms of interleukin-19 treatment on AAAs were assessed via ultrasonography, histopathology, flow cytometry, and gene expression profiling. Immunohistochemistry revealed augmented interleukin-19 expression in both human and experimental AAAs. In mice, interleukin-19 treatment before AAA initiation via elastase infusion suppressed aneurysm formation and progression, with attenuation of medial elastin degradation, smooth-muscle depletion, leukocyte infiltration, neoangiogenesis, and matrix metalloproteinase 2 and 9 expression. Initiation of interleukin-19 treatment after AAA creation limited further aneurysmal degeneration. In additional experiments, interleukin-19 treatment inhibited murine macrophage recruitment following intraperitoneal thioglycolate injection. In classically or alternatively activated macrophages in vitro, interleukin-19 downregulated mRNA expression of inducible nitric oxide synthase, chemokine C-C motif ligand 2, and metalloproteinases 2 and 9 without apparent effect on cytokine-expressing helper or cytotoxic T-cell differentiation, nor regulatory T cellularity, in the aneurysmal aorta or spleen of interleukin-19-treated mice. Interleukin-19 also suppressed AAAs created via angiotensin II infusion in hyperlipidemic mice. Conclusions Based on human evidence and experimental modeling observations, interleukin-19 may influence the development and progression of AAAs.