The durability of oral diabetic medications: Time to A1c baseline and a review of common oral medications used by the primary care provider.

Introduction:Cost of generic medications has risen more in the past few years than any other time in history. While medical insurance covers much of these costs, health care professionals can better provide medications that have the longest duration of action when compared to placebo-treated controls. This will save health care costs and improve prescribing accuracy. Methods:Papers in PubMed were identified with keywords placebo. The study must be at least 2 years in length to evaluate the change in A1c over time. The primary endpoint was time to A1c neutrality (return of A1c to baseline at a maximum dose of single oral agent). A medication would be considered at neutrality if the 95% CI crossed baseline. Time to neutrality was averaged for each medication within the class and each summarized for class effect. Results:Effective therapy for the DPP-4 and sulfonylurea classes of medications are 3-4 years as compared to a 5-year time to A1c neutrality for metformin usage. In comparison, the projected time to A1c neutrality was approximately 6-8 years for rosiglitazone and pioglitazone. While only a few studies have been published in the SGLT-2 class of medication, the time to A1c neutrality was also 6-8 years with Canagliflozin and full dosage of Empagliflozin. Conclusion:Metformin appears to have a 5-year duration of effect before the A1c returns to baseline. The sulfonylureas and DPP-4 inhibitors class of medications have one of the shortest durability which ranges between 3.3 to 4.4 years. In contrast, the SGLT-2 class of medication and the TZD class of medications has a projected time to A1c neutrality from 6-8 years. Diabetic duration of therapy as compared to placebo should be listed with those medications tested so the provider can choose wisely

Making Arts Programming Accessible

https://remix.berklee.edu/able-assembly-conference/1079/thumbnail.jp

A False Start in the Race Against Doping in Sport: Concerns With Cycling’s Biological Passport

Professional cycling has suffered from a number of doping scandals. The sport’s governing bodies have responded by implementing an aggressive new antidoping program known as the biological passport. Cycling’s biological passport marks a departure from traditional antidoping efforts, which have focused on directly detecting prohibited substances in a cyclist’s system. Instead, the biological passport tracks biological variables in a cyclist’s blood and urine over time, monitoring for fluctuations that are thought to indirectly reveal the effects of doping. Although this method of indirect detection is promising, it also raises serious legal and scientific concerns. Since its introduction, the cycling community has debated the reliability of indirect biological-passport evidence and the clarity, consistency, and transparency of its use in proving doping violations. Such uncertainty undermines the legitimacy of finding cyclists guilty of doping based on this indirect evidence alone. Antidoping authorities should address these important concerns before continuing to pursue doping sanctions against cyclists solely on the basis of their biological passports

Effect of carnitine on muscular glutamate uptake and intramuscular glutathione in malignant diseases

Abnormally low intramuscular glutamate and glutathione (GSH) levels and/or a decreased muscular uptake of glutamate by the skeletal muscle tissue have previously been found in malignant diseases and simian immunodeficiency virus (SIV) infection and may contribute to the development of cachexia. We tested the hypothesis that an impaired mitochondrial energy metabolism may compromise the Na+-dependent glutamate transport. A randomized double-blind clinical trial was designed to study the effects of L -carnitine, i.e. an agent known to enhance mitochondrial integrity and function, on the glutamate transport and plasma glutamate level of cancer patients. The effect of carnitine on the intramuscular glutamate and GSH levels was examined in complementary experiments with tumour-bearing mice. In the mice, L -carnitine treatment ameliorated indeed the tumour-induced decrease in muscular glutamate and GSH levels and the increase in plasma glutamate levels. The carnitine-treated group in the randomized clinical study showed also a significant decrease in the plasma glutamate levels but only a moderate and statistically not significant increase in the relative glutamate uptake in the lower extremities. Further studies may be warranted to determine the effect of L -carnitine on the intramuscular GSH levels in cancer patients. © 2000 Cancer Research Campaig

FDG–PET. A possible prognostic factor in head and neck cancer

Previous studies have shown that high uptake of 18F-fluoro-2-deoxy-glucose in head and neck cancer, as determined by the standardized uptake value on positron emission tomography scan, was associated with poor survival. The aim of this study was to confirm the association and to establish whether a high standardized uptake value had prognostic significance. Seventy-three consecutive patients with newly diagnosed squamous cell carcinoma of the head and neck underwent a positron emission tomography study before treatment. Age, gender, performance status tumour grade, stage, maximal tumour diameter and standardized uptake value were analyzed for their possible association with survival. The median standardized uptake value for all primary tumours was 7.16 (90% range 2.30 to 18.60). In univariate survival analysis the cumulative survival was decreased as the stage, tumour diameter and standardized uptake value increased. An standardized uptake value of 10 was taken as a cut-off for high and low uptake tumours. When these two groups were compared, an standardized uptake value >10 predicted for significantly worse outcome (P=0.003). Multivariate analysis demonstrated that an standardized uptake value >10 provided prognostic information independent of the tumour stage and diameter (P=0.002). We conclude that high FDG uptake (standardized uptake value>10) on positron emission tomography is an important marker for poor outcome in primary squamous cell carcinoma of the head and neck. Standardized uptake value may be useful in distinguishing those tumours with a more aggressive biological nature and hence identifying patients that require intensive treatment protocols including hyperfractionated radiotherapy and/or chemotherapy

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−(13)C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown

Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning.

Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease

Potential organ or tumor imaging agents. 32. A triglyceride ester of P-Iodophenyl pentadecanoic acid as a potential hepatic imaging agent

A triglyceride analog, glycerol-2-palmitoyl-1,3-di-15-(p-iodophenyl)pentadecanoate (DPPG) was synthesized and radiolabeled for evaluation as a potential functional liver scintigraphic agent. Uptake of DPPG was compared in normal, diabetic, tumor-bearing and heparin pretreated rats, revealing differences in uptake and clearance of radioactivity, correlating with hepatic lipase activity of these groups. Similar results were observed by [gamma] -camera scintigraphy. Comparing the uptake of DPPG with that of its fatty acid component, 15-(p-iodophenyl)pentadecanoic acid (IPPA), revealed that the peak uptake of IPPA in the liver was about half that of DPPG. Based upon these findings, DPPG warrants further study as a hepatic radiodiagnostic agent.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29932/1/0000289.pd

Quantitative imaging biomarkers of coronary plaque morphology: insights from EVAPORATE

AimsResidual cardiovascular risk persists despite statin therapy. In REDUCE-IT, icosapent ethyl (IPE) reduced total events, but the mechanisms of benefit are not fully understood. EVAPORATE evaluated the effects of IPE on plaque characteristics by coronary computed tomography angiography (CCTA). Given the conclusion that the IPE-treated patients demonstrate that plaque burden decreases has already been published in the primary study analysis, we aimed to demonstrate whether the use of an analytic technique defined and validated in histological terms could extend the primary study in terms of whether such changes could be reliably seen in less time on drug, at the individual (rather than only at the cohort) level, or both, as neither of these were established by the primary study result.Methods and ResultsEVAPORATE randomized the patients to IPE 4 g/day or placebo. Plaque morphology, including lipid-rich necrotic core (LRNC), fibrous cap thickness, and intraplaque hemorrhage (IPH), was assessed using the ElucidVivo® (Elucid Bioimaging Inc.) on CCTA. The changes in plaque morphology between the treatment groups were analyzed. A neural network to predict treatment assignment was used to infer patient representation that encodes significant morphological changes. Fifty-five patients completed the 18-month visit in EVAPORATE with interpretable images at each of the three time points. The decrease of LRNC between the patients on IPE vs. placebo at 9 months (reduction of 2 mm3 vs. an increase of 41 mm3, p = 0.008), widening at 18 months (6 mm3 vs. 58 mm3 increase, p = 0.015) were observed. While not statistically significant on a univariable basis, reductions in wall thickness and increases in cap thickness motivated multivariable modeling on an individual patient basis. The per-patient response assessment was possible using a multivariable model of lipid-rich phenotype at the 9-month follow-up, p < 0.01 (sustained at 18 months), generalizing well to a validation cohort.ConclusionPlaques in the IPE-treated patients acquired more characteristics of stability. Reliable assessment using histologically validated analysis of individual response is possible at 9 months, with sustained stabilization at 18 months, providing a quantitative basis to elucidate drug mechanism and assess individual patient response