Clinical Factors Associated with High Glycemic Variability Defined by Coefficient of Variation in Patients with Type 2 Diabetes.

BACKGROUND: High glycemic Variability (HGV) has become a stronger predictor of hypoglycemia. However, clinical factors associate with HGV still are unknown. OBJECTIVE: To determine clinical variables that were associated with a coefficient of variation (CV) above 36% evaluated by continuous glucose monitoring (CGM) in a group of patients with diabetes mellitus. METHODS: A cohort of patients with type 2 diabetes (T2D) was evaluated. Demographic variables, HbA1c, glomerular filtration rate (GFR) and treatment regimen were assessed. A bivariate analysis was performed, to evaluate the association between the outcome variable (CV> 36%) and each of the independent variables. A multivariate model was constructed to evaluate associations after controlling for confounding variables. RESULTS: CGM data from 274 patients were analyzed. CV> 36% was present in 56 patients (20.4%). In the bivariate analysis, demographic and clinical variables were included, such as time since diagnosis, hypoglycemia history, A1c, GFR and treatment established. In the multivariate analysis, GFR <45 mL/min (OR 2.81; CI 1.27,6.23; p:0.01), A1c > 9% (OR 2.81; CI 1.05,7.51; p:0.04) and hypoglycemia history (OR 2.09; CI 1.02,4.32; p:0.04) were associated with HGV. Treatment with iDPP4 (OR 0.39; CI 0.19,0.82; p:0.01) and AGLP1 (OR 0.08; CI 0.01,0.68; p:0.02) was inversely associated with GV. CONCLUSION: Clinical variables such as GFR <45 mL/min, HbA1C>9% and a history of hypoglycemia are associated with a high GV. Our data suggest that the use of technology and treatments able to reduce glycemic variability could be useful in this population to reduce the risk of hypoglycemia and to improve glycemic control.

Dissipation and effects of tricyclazole on soil microbial communities and rice growth as affected by amendment with alperujo compost.

The presence of pesticides in surface and groundwater has grown considerably in the last decades as a consequence of the intensive farming activity. Several studies have shown the benefits of using organic amendments to prevent losses of pesticides from runoff or leaching. A particular soil from the Guadalquivir valley was placed in open air ponds and amended at 1 or 2% (w/w) with alperujo compost (AC), a byproduct from the olive oil industry. Tricyclazole dissipation, rice growth and microbial diversity were monitored along an entire rice growing season. An increase in the net photosynthetic rate of Oryza sativa plants grown in the ponds with AC was observed. These plants produced between 1100 and 1300kgha(-1) more rice than plants from the unamended ponds. No significant differences were observed in tricyclazole dissipation, monitored for a month in soil, surface and drainage water, between the amended and unamended ponds. The structure and diversity of bacteria and fungi communities were also studied by the use of the polymerase chain reaction denaturing gel electrophoresis (PCR-DGGE) from DNA extracted directly from soil samples. The banding pattern was similar for all treatments, although the density of bands varied throughout the time. Apparently, tricyclazole did not affect the structure and diversity of bacteria and fungi communities, and this was attributed to its low bioavailability. Rice cultivation under paddy field conditions may be more efficient under the effects of this compost, due to its positive effects on soil properties, rice yield, and soil microbial diversity.

Influence of green waste compost on azimsulfuron dissipation and soil functions under oxic and anoxic conditions.

Concerns have been raised over the sustainability of intensive rice cultivation, where the use of chemical fertilizers and pesticides has been associated with numerous environmental problems. The objective of this study was to test the effect of the herbicide azimsulfuron on important soil functions as affected by amendment with a byproduct of the olive oil industry. Soil was collected from a Mediterranean rice field. Part of it was amended with alperujo compost (AC). Amended and unamended soils were incubated for 43days in presence or not of azimsulfuron, under anoxic-flooded (AF) and oxic-unflooded (OU) conditions. We monitored the dissipation of the herbicide azimsulfuron, C mineralization, soil microbial biomass (SMB) and dissolved organic carbon (DOC) content and its nature. Under AF conditions, the application of compost produced an increase in the dissipation of the herbicide (up to 12.4%). It was related with the higher DOC content, 4 times higher than under OU conditions. Though increases in carbon turnover (under AF and OU conditions) and reduction of SMBC after herbicide application (only under AF conditions) were observed, the differences were not statistically significant. The application of this organic amendment is presented as an efficient management strategy to increase C turnover in agricultural soils and reduce some of the negative effects derived from the application of azimsulfuron under flooded conditions.

Effect of soil organic amendments on the behavior of bentazone and tricyclazole.

The effect of soil amendment with different organic residues from olive oil production on the sorption and leaching of two pesticides used in rice crops (bentazone and tricyclazole) was compared in order to understand their behavior and to improve soil properties by recycling an abundant agricultural residue in Andalucía (S. Spain). A residue from olive oil production (AJ), the organic compost derived from this organic waste (CA) and a biochar (BA) made from CA were used. A soil devoted to rice cultivation, IFAPA (I), was amended at 2% (w/w) of each amendment individually (I+AJ, I+CA and I+BA). In order to evaluate the effect of dissolved organic matter (DOM) from these amendments on bentazone and tricyclazole behavior, the DOM from the amendments was extracted, quantified and characterized by fluorescence spectroscopy and FT-IR. The affinity of DOM for soil surfaces was evaluated with (I) soil and two other soils of different physicochemical properties, ARCO (A) and GUAD (G). These studies revealed differences in DOM quantity, quality and affinity for the used soils among amendments which can explain the different sorption behavior observed for tricyclazole in the amended soils. Leaching assays under saturated/unsaturated conditions revealed a slight delay of bentazone in I+CA and I+BA soils when compared to I+AJ, that can be related to the higher DOM content and much lower specific surface area of AJ. In contrast, tricyclazole was not detected in any of the leachates during the leaching assay. Extraction of tricyclazole residues from soil columns showed that the fungicide did not move below 5cm in the higher sorptive systems (I+CA, I+BA). The sorption of DOM from amendments on soil during the transport process can decrease the mobility of the fungicide by changing the physicochemical properties of the soil surface whose behavior may be dominated by the adsorbed DOM.

Prediction of postprandial blood glucose under uncertainty and intra-patient variability in type 1 diabetes: a comparative study of three interval models.

The behavior of three insulin action and glucose kinetics models was assessed for an insulin therapy regime in the presence of patient variability. For this purpose, postprandial glucose in patients with type 1 diabetes was predicted by considering intra- and inter-patient variability using modal interval analysis. Equations to achieve optimal prediction are presented for models 1, 2 and 3, which are of increasing complexity. The model parameters were adjusted to reflect the "same" patient in the presence of variability. The glucose response envelope for model 1, the simplest insulin-glucose model assessed, included the responses of the other two models when a good fit of the model parameters was achieved. Thus, under variability, simple glucose-insulin models may be sufficient to describe patient dynamics in most situations.

Insulin dosage optimization based on prediction of postprandial glucose excursions under uncertain parameters and food intake.

Considering the difficulty in selecting correct insulin doses and the problem of hyper- and hypoglycemia episodes in type 1 diabetes, dosage-aid systems are very useful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as large intra-patient variability and food intake. In the present study, postprandial glucose is predicted considering this uncertain information using modal interval analysis. This approach calculates a safer prediction of possible hyper- and hypoglycemia episodes induced by insulin therapy for an individual patient's parameters and integrates this information into a dosage-aid system. Predictions of a patient's postprandial glucose at 5-h intervals are used to predict the risk for a given therapy. Then the insulin dose and injection-to-meal time with the lowest risk are calculated. The method has been validated for three different scenarios corresponding to preprandial glucose values of 100, 180 and 250mg/dl.

Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus.

In this paper, the problem of tackling uncertainty in the prediction of postprandial blood glucose is analyzed. Two simulation approaches, Monte Carlo and interval models, are studied and compared. Interval simulation is carried out using modal interval analysis. Simulation of a glucoregulatory model with uncertainty in insulin sensitivities, glucose absorption and food intake is carried out using both methods. Interval simulation is superior in predicting all severe and mild hyper- and hypoglycemia episodes. Furthermore, much less computational time is required for interval simulation than for Monte Carlo simulation.

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus.

Considering the difficulty in the insulin dosage selection and the problem of hyper-and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper-and hypoglycaemia episodes can be calculated.