Failure to Preserve β-Cell Function With Mycophenolate Mofetil and Daclizumab Combined Therapy in Patients With New- Onset Type 1 Diabetes

OBJECTIVE This trial tested whether mycophenolate mofetil (MMF) alone or with daclizumab (DZB) could arrest the loss of insulin-producing β-cells in subjects with new-onset type 1 diabetes. RESEARCH DESIGN AND METHODS A multi-center, randomized, placebo-controlled, double-masked trial was initiated by Type 1 Diabetes TrialNet at 13 sites in North America and Europe. Subjects diagnosed with type 1 diabetes and with sufficient C-peptide within 3 months of diagnosis were randomized to either MMF alone, MMF plus DZB, or placebo, and then followed for 2 years. The primary outcome was the geometric mean area under the curve (AUC) C-peptide from the 2-h mixed meal tolerance test. RESULTS One hundred and twenty-six subjects were randomized and treated during the trial. The geometric mean C-peptide AUC at 2 years was unaffected by MMF alone or MMF plus DZB versus placebo. Adverse events were more frequent in the active therapy groups relative to the control group, but not significantly. CONCLUSIONS Neither MMF alone nor MMF in combination with DZB had an effect on the loss of C-peptide in subjects with new-onset type 1 diabetes. Higher doses or more targeted immunotherapies may be needed to affect the autoimmune process

Antithymocyte Globulin Plus G-CSF Combination Therapy Leads to Sustained Immunomodulatory and Metabolic Effects in a Subset of Responders With Established Type 1 Diabetes.

Low-dose antithymocyte globulin (ATG) plus pegylated granulocyte colony-stimulating factor (G-CSF) preserves β-cell function for at least 12 months in type 1 diabetes. Herein, we describe metabolic and immunological parameters 24 months following treatment. Patients with established type 1 diabetes (duration 4-24 months) were randomized to ATG and pegylated G-CSF (ATG+G-CSF) (N = 17) or placebo (N = 8). Primary outcomes included C-peptide area under the curve (AUC) following a mixed-meal tolerance test (MMTT) and flow cytometry. "Responders" (12-month C-peptide ≥ baseline), "super responders" (24-month C-peptide ≥ baseline), and "nonresponders" (12-month C-peptide < baseline) were evaluated for biomarkers of outcome. At 24 months, MMTT-stimulated AUC C-peptide was not significantly different in ATG+G-CSF (0.49 nmol/L/min) versus placebo (0.29 nmol/L/min). Subjects treated with ATG+G-CSF demonstrated reduced CD4+ T cells and CD4+/CD8+ T-cell ratio and increased CD16+CD56hi natural killer cells (NK), CD4+ effector memory T cells (Tem), CD4+PD-1+ central memory T cells (Tcm), Tcm PD-1 expression, and neutrophils. FOXP3+Helios+ regulatory T cells (Treg) were elevated in ATG+G-CSF subjects at 6, 12, and 18 but not 24 months. Immunophenotyping identified differential HLA-DR expression on monocytes and NK and altered CXCR3 and PD-1 expression on T-cell subsets. As such, a group of metabolic and immunological responders was identified. A phase II study of ATG+G-CSF in patients with new-onset type 1 diabetes is ongoing and may support ATG+G-CSF as a prevention strategy in high-risk subjects

Treatment recommendations for psoriatic arthritis

Objective: To develop comprehensive recommendations for the treatment of the various clinical manifestations of psoriatic arthritis (PsA) based on evidence obtained from a systematic review of the literature and from consensus opinion. Methods: Formal literature reviews of treatment for the most significant discrete clinical manifestations of PsA (skin and nails, peripheral arthritis, axial disease, dactylitis and enthesitis) were performed and published by members of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA). Treatment recommendations were drafted for each of the clinical manifestations by rheumatologists, dermatologists and PsA patients based on the literature reviews and consensus opinion. The level of agreement for the individual treatment recommendations among GRAPPA members was assessed with an online questionnaire. Results: Treatment recommendations were developed for peripheral arthritis, axial disease, psoriasis, nail disease, dactylitis and enthesitis in the setting of PsA. In rotal, 19 recommendations were drafted, and over 80% agreement was obtained on 16 of them. In addition, a grid that factors disease severity into each of the different disease manifestations was developed to help the clinician with treatment decisions for the individual patient from an evidenced-based perspective. Conclusions: Treatment recommendations for the cardinal physical manifestations of PsA were developed based on a literature review and consensus between rheumatologists and dermatologists. In addition, a grid was established to assist in therapeutic reasoning and decision making for individual patients. It is anticipated that periodic updates will take place using this framework as new data become available

Reversed argininosuccinate lyase activity in fumarate hydratase-deficient cancer cells.

BACKGROUND: Loss of function of fumarate hydratase (FH), the mitochondrial tumor suppressor and tricarboxylic acid (TCA) cycle enzyme, is associated with a highly malignant form of papillary and collecting duct renal cell cancer. The accumulation of fumarate in these cells has been linked to the tumorigenic process. However, little is known about the overall effects of the loss of FH on cellular metabolism. METHODS: We performed comprehensive metabolomic analyses of urine from Fh1-deficient mice and stable isotopologue tracing from human and mouse FH-deficient cell lines to investigate the biochemical signature of the loss of FH. RESULTS: The metabolomics analysis revealed that the urea cycle metabolite argininosuccinate is a common metabolic biomarker of FH deficiency. Argininosuccinate was found to be produced from arginine and fumarate by the reverse activity of the urea cycle enzyme argininosuccinate lyase (ASL), making these cells auxotrophic for arginine. Depleting arginine from the growth media by the addition of pegylated arginine deiminase (ADI-PEG 20) decreased the production of argininosuccinate in FH-deficient cells and reduced cell survival and proliferation. CONCLUSIONS: These results unravel a previously unidentified correlation between fumarate accumulation and the urea cycle enzyme ASL in FH-deficient cells. The finding that FH-deficient cells become auxotrophic for arginine opens a new therapeutic perspective for the cure of hereditary leiomyomatosis and renal cell cancer (HLRCC).RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Lattice results for the decay constant of heavy-light vector mesons

We compute the leptonic decay constants of heavy-light vector mesons in the quenched approximation. The reliability of lattice computations for heavy quarks is checked by comparing the ratio of vector to pseudoscalar decay constant with the prediction of Heavy Quark Effective Theory in the limit of infinitely heavy quark mass. Good agreement is found. We then calculate the decay constant ratio for B mesons: $f_{B^*}/f_B= 1.01(0.01)(^{+0.04}_{-0.01})$. We also quote quenched $f_{B^*}=177(6)(17)$ MeV.Comment: 11 pages, 3 postscript figs., revtex; two references adde

Exenatide extended release in patients with type 1 diabetes with and without residual insulin production

AimsTo test whether a long- acting GLP- 1 receptor agonist would improve glucose control in patients with type 1 diabetes (T1D) and to determine whether the presence of residual beta cell function would affect the response. In addition, we sought to determine whether the drug would affect beta cell function.MethodsWe performed a randomized placebo- controlled trial of exenatide extended release (ER) in participants with T1D with and without detectable levels of C- peptide. Seventy- nine participants were randomized to exenatide ER 2 mcg weekly, or placebo, stratified by the presence or absence of detectable C- peptide levels. The primary outcome was the difference in glycated haemoglobin (HbA1c) levels at 24- weeks. Participants were followed for another 6 months off study drug.ResultsAt week 24, the time of the primary outcome, the least squares (LS) mean HbA1c level was 7.76% (95% confidence interval [CI] 7.42, 8.10) in the exenatide ER group versus 8.0% (95% CI 7.64, 8.35) in the placebo group (P = 0.08). At week 12 the LS mean HbA1c levels were 7.71% (95% CI 7.37, 8.05) in the exenatide ER group versus 8.05% (95% CI 7.7, 8.4) in the placebo group (P = 0.01). The improvement at week 12 was driven mainly by those with detectable levels of C- peptide. Those treated with exenatide ER lost weight at 12 and 24- weeks compared to those treated with placebo (P- <0.001 and P = 0.007). The total insulin dose was lower, but not when corrected for body weight, and was not affected by residual insulin production. Adverse events were more frequent with exenatide ER, but hypoglycaemia was not increased.ConclusionTreatment with exenatide ER may have short- term benefits in some individuals with T1D who are overweight or who have detectable levels of C- peptide, but short- term improvements were not sustained.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163873/1/dom14121_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163873/2/dom14121.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163873/3/dom14121-sup-0001-Supinfo.pd

Fall in C-peptide during first 2 years from diagnosis: Evidence of at least two distinct phases from composite type 1 diabetes trialnet data.

Interpretation of clinical trials to alter the decline in β-cell function after diagnosis of type 1 diabetes depends on a robust understanding of the natural history of disease. Combining data from the Type 1 Diabetes TrialNet studies, we describe the natural history of β-cell function from shortly after diagnosis through 2 years post study randomization, assess the degree of variability between patients, and investigate factors that may be related to C-peptide preservation or loss. We found that 93% of individuals have detectable C-peptide 2 years from diagnosis. In 11% of subjects, there was no significant fall from baseline by 2 years. There was a biphasic decline in C-peptide; the C-peptide slope was −0.0245 pmol/mL/month (95% CI −0.0271 to −0.0215) through the first 12 months and −0.0079 (−0.0113 to −0.0050) from 12 to 24 months (P \u3c 0.001). This pattern of fall in C-peptide over time has implications for understanding trial results in which effects of therapy are most pronounced early and raises the possibility that there are time-dependent differences in pathophysiology. The robust data on the C-peptide obtained under clinical trial conditions should be used in planning and interpretation of clinical trials

CD127 expression inversely correlates with FoxP3 and suppressive function of human CD4+ T reg cells

Regulatory T (T reg) cells are critical regulators of immune tolerance. Most T reg cells are defined based on expression of CD4, CD25, and the transcription factor, FoxP3. However, these markers have proven problematic for uniquely defining this specialized T cell subset in humans. We found that the IL-7 receptor (CD127) is down-regulated on a subset of CD4+ T cells in peripheral blood. We demonstrate that the majority of these cells are FoxP3+, including those that express low levels or no CD25. A combination of CD4, CD25, and CD127 resulted in a highly purified population of T reg cells accounting for significantly more cells that previously identified based on other cell surface markers. These cells were highly suppressive in functional suppressor assays. In fact, cells separated based solely on CD4 and CD127 expression were anergic and, although representing at least three times the number of cells (including both CD25+CD4+ and CD25−CD4+ T cell subsets), were as suppressive as the “classic” CD4+CD25hi T reg cell subset. Finally, we show that CD127 can be used to quantitate T reg cell subsets in individuals with type 1 diabetes supporting the use of CD127 as a biomarker for human T reg cells

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P&lt;0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP