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

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

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

Diabetes patients and non-diabetic patients intensive care unit and hospital mortality risks associated with sepsis

AIM: To compare mortality risks associated with known diabetic patients to hyperglycemic non-diabetic patients

Importance of fasting blood glucose goals in the management of type 2 diabetes mellitus: a review of the literature and a critical appraisal.

Prandial insulin has been essential for the improved management of the type 1 diabetic patient. Interestingly, many studies have evaluated the addition of prandial insulin to the type 2 diabetic patients with improved control. The greatest drop in A1c with the use of various type of prandial insulins have resulted in the decrease of 1.3% in the A1c measurement. Interestingly, none of the published trials with goal of fasting blood glucose (FBG) have ever obtained the goal A1c. Since a drop in FBG of 28.7mg/dl is equal to a 1% drop in A1c, a simple approach to obtain a target A1c would be to focus on the FBG (per ADA: Average Blood Glucose = A1c (%) x 28.7 - 46.7mg/d). However, average blood glucose requires multiple measurements and may be less accurate then using just a FBG. Since prandial insulin clinical trials have only demonstrated a drop in A1c by 0.3-1.3% the use of only a FBG to help patients get to goal may be easier to teach and to obtain. It might save time and money. Our hypothesis is that if patient obtain a FBG <100 mg/dl for 2-3 months then 70% will be at an A1c goal <7.0%. After a few months of good fasting glucose control the provider can use this equation (FBG+80)/30 to estimate A1c. For example, a FBG of 130mg/dl would be (130 + 80)/30 = 7.0%; or a FBG of 190 would be (190+80)/30 =eA1c 9% (estimate of A1c). While type 1 diabetes has a very complex daily glucose pattern, the approach to type 2 diabetics on insulin could become simplified

Weight loss and diabetes are new risk factors for the development of invasive aspergillosis infection in non-immunocompromized humans.

Well-established risk factors for aspergillosis include HIV, cancer, recent corticosteroid (prednisone) therapy, chemotherapy, or thoracic surgery. Non-established risk factors may include weight loss and a history of diabetes. Twenty-three patients without the classical risk factors for IA were identified retrospectively at Harbor UCLA Medical Center by discharge diagnosis over a 20 year period (1992-2012). None of the well-known risk factors are for Invasive Apergillious (IA). A history of weight loss was seen in 66% of the patients with IA (15 of 23). The weight loss ranged from 3.3 lbs to 43 lbs. In patients with weight loss the average loss was 22±3 lbs (mean±SEM). In this small group of patients with IA, diabetes was seen in 8 of the 23 (34%), which is significantly higher than the 19% incidence of diabetes seen in 100 patients with severe sepsis (p<0.05). Likewise, the 34% incidence of diabetes was higher than the 21% incidence reported in immunocompromised patients with invasive aspergillus (IA) infection (p<0.05). A reduced serum albumin concentration was seen in 33% of the study patients, which was less common than the 87% incidence seen in patients with severe sepsis or candidaemia (54%). Seventeen of the 23 patients had pulmonary involvement. While no one had a well-established risk factor for aspergillious, four patients had alcoholism as a potential risk factor. Eleven of the 23 (48%) died during the hospital stay despite antifungal therapy. Immunocompromised patients are known to have a mortality rate of approximately 45% for pulmonary or disseminated disease.ConclusionThe incidence of diabetes was greater than seen in immunocompromised patients and may be considered an additional risk factor for the development of aspergillois infection. In addition, a history of weight loss should increase the suspicion for the diagnosis of IA in otherwise a non-immunocompromised patient. Early recognition and treatment of aspergillosis in the non-immunocompromised patient may improve outcome. Weight loss and diabetes should be added to the list of well-known risk factors for invasive aspergillosis and its high mortality rate

Importance of fasting blood glucose goals in the management of type 2 diabetes mellitus: a review of the literature and a critical appraisal.

Prandial insulin has been essential for the improved management of the type 1 diabetic patient. Interestingly, many studies have evaluated the addition of prandial insulin to the type 2 diabetic patients with improved control. The greatest drop in A1c with the use of various type of prandial insulins have resulted in the decrease of 1.3% in the A1c measurement. Interestingly, none of the published trials with goal of fasting blood glucose (FBG) have ever obtained the goal A1c. Since a drop in FBG of 28.7mg/dl is equal to a 1% drop in A1c, a simple approach to obtain a target A1c would be to focus on the FBG (per ADA: Average Blood Glucose = A1c (%) x 28.7 - 46.7mg/d). However, average blood glucose requires multiple measurements and may be less accurate then using just a FBG. Since prandial insulin clinical trials have only demonstrated a drop in A1c by 0.3-1.3% the use of only a FBG to help patients get to goal may be easier to teach and to obtain. It might save time and money. Our hypothesis is that if patient obtain a FBG <100 mg/dl for 2-3 months then 70% will be at an A1c goal <7.0%. After a few months of good fasting glucose control the provider can use this equation (FBG+80)/30 to estimate A1c. For example, a FBG of 130mg/dl would be (130 + 80)/30 = 7.0%; or a FBG of 190 would be (190+80)/30 =eA1c 9% (estimate of A1c). While type 1 diabetes has a very complex daily glucose pattern, the approach to type 2 diabetics on insulin could become simplified

Weight loss and diabetes are new risk factors for the development of invasive aspergillosis infection in non-immunocompromized humans.

Well-established risk factors for aspergillosis include HIV, cancer, recent corticosteroid (prednisone) therapy, chemotherapy, or thoracic surgery. Non-established risk factors may include weight loss and a history of diabetes. Twenty-three patients without the classical risk factors for IA were identified retrospectively at Harbor UCLA Medical Center by discharge diagnosis over a 20 year period (1992-2012). None of the well-known risk factors are for Invasive Apergillious (IA). A history of weight loss was seen in 66% of the patients with IA (15 of 23). The weight loss ranged from 3.3 lbs to 43 lbs. In patients with weight loss the average loss was 22±3 lbs (mean±SEM). In this small group of patients with IA, diabetes was seen in 8 of the 23 (34%), which is significantly higher than the 19% incidence of diabetes seen in 100 patients with severe sepsis (p<0.05). Likewise, the 34% incidence of diabetes was higher than the 21% incidence reported in immunocompromised patients with invasive aspergillus (IA) infection (p<0.05). A reduced serum albumin concentration was seen in 33% of the study patients, which was less common than the 87% incidence seen in patients with severe sepsis or candidaemia (54%). Seventeen of the 23 patients had pulmonary involvement. While no one had a well-established risk factor for aspergillious, four patients had alcoholism as a potential risk factor. Eleven of the 23 (48%) died during the hospital stay despite antifungal therapy. Immunocompromised patients are known to have a mortality rate of approximately 45% for pulmonary or disseminated disease.ConclusionThe incidence of diabetes was greater than seen in immunocompromised patients and may be considered an additional risk factor for the development of aspergillois infection. In addition, a history of weight loss should increase the suspicion for the diagnosis of IA in otherwise a non-immunocompromised patient. Early recognition and treatment of aspergillosis in the non-immunocompromised patient may improve outcome. Weight loss and diabetes should be added to the list of well-known risk factors for invasive aspergillosis and its high mortality rate