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Mathematical model of glucagon kinetics during an oral glucose tolerance test based on a dual regulation mechanism
No full textGlucagon is a hormone secreted by the pancreatic alpha cells and plays a key role in glucose homeostasis and in the pathophysiology of type 2 diabetes (T2D). Different mechanisms are involved in its regulation, but exact mechanisms are still largely unknown. This study aimed to propose a model describing glucagon inhibition during an oral glucose tolerance test (OGTT), accounting for a double regulation mechanism. The model has been developed starting from a model previously proposed by our group that includes two differential equations, one for plasma glucagon and one for C-peptide (marker of insulin at pancreatic level). In the new model, in addition to plasma C-peptide, plasma glucose is included as model input. The model provides two parameters of possible clinical relevance, namely SGLUCA and kG (alpha-cell insulin and glucose sensitivity, respectively) and has been validated on mean literature data of healthy subjects and subjects affected by T2D (CNT and T2D, respectively). Model analysis yielded SGLUCA estimates ranging from -0.1515 to 0.7629 and from -5.5602 to 1.1067 (ng of glucagon·nmol of C-peptide-1) in CNT and T2D groups, respectively; according to the 95% confidence intervals (CIs), SGLUCA was significantly different from zero in 4 and in 0 out of 8 time points, in CNT and T2D. Estimates for kG were equal to 2.8302 (95% CIs: 1.1973-4.4632) and 0.9913 (95% CIs: -0.5559-2.5386) ng of glucagon·mmol of glucose-1. Thus, results suggest both insulin (represented by C-peptide) and glucose significantly contributes to glucagon inhibition in healthy subjects, but not in T2D. In conclusion, the proposed model may help to describe different mechanisms acting on glucagon inhibition in the single individuals
Availability of Open Dynamic Glycemic Data in the Field of Diabetes Research: A Scoping Review
No full textBackground: Poor data availability and accessibility characterizing some research areas in biomedicine are still limiting potentialities for increasing knowledge and boosting technological advancement. This phenomenon also characterizes the field of diabetes research, in which glycemic data may serve as a basis for different applications. To overcome this limitation, this review aims to provide a comprehensive analysis of the publicly available data sets related to dynamic glycemic data. Methods: Search was performed in four different sources, namely scientific journals, Google, a comprehensive registry of clinical trials and two electronic databases. Retrieved data sets were analyzed in terms of their main characteristics and on the typology of data provided. Results: Twenty-five data sets were identified including data from challenge tests (5 of 25) or data from Continuous Glucose Monitoring (CGM, 20 of 25). As for the data sets including challenge tests, all of them were freely downloadable; most of them (80%) related only to oral glucose tolerance test (OGTT) with standard duration (2 h), but varying for timing and number of collected blood samples, and variables collected in addition to glucose levels (with insulin levels being the most common); the remaining 20% of them also included intravenous glucose tolerance test (IVGTT) data. As for the data sets related to CGM, 7 of 20 were freely downloadable, whereas the remaining 13 were downloadable upon completion of a request form. Conclusions: This review provided an overview of the readily usable data sets, thus representing a step forward in fostering data access in diabetes field
Sensors and Devices Based on Electrochemical Skin Conductance and Bioimpedance Measurements for the Screening of Diabetic Foot Syndrome: Review and Meta-Analysis
Full text linkDiabetic foot syndrome is a multifactorial disease involving different etiological factors. This syndrome is also insidious, due to frequent lack of early symptoms, and its prevalence has increased in recent years. This justifies the remarkable attention being paid to the syndrome, although the problem of effective early screening for this syndrome, possibly at a patient’s home, is still unsolved. However, some options appear available in this context. First, it was demonstrated that the temperature measurement of the foot skin is an interesting approach, but it also has some limitations, and hence a more effective approach should combine data from temperature and from other sensors. For this purpose, foot skin conductance or bioimpedance measurement may be a good option. Therefore, the aim of this study was to review those studies where skin conductance/bioimpedance measurement was used for the detection of diabetic foot syndrome. In addition, we performed a meta-analysis of some of those studies, where a widely used device was exploited (SUDOSCAN®) for foot skin conductance measurement, and we found that skin conductance levels can clearly distinguish between groups of patients with and without diabetic neuropathy, the latter being one of the most relevant factors in diabetic foot syndrome
Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques
Full text linkGestational diabetes mellitus (GDM) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 59) progress to T2DM has been performed by using Orange software through a data analysis procedure on a generated data set including anthropometric data and a total of 34 features, extracted through mathematical modeling/methods procedures. Feature selection has been performed through decision tree algorithm and then Naïve Bayes and penalized (L2) logistic regression were used to evaluate the ability of the selected features to solve the classification problem. Performance has been evaluated in terms of area under the operating receiver characteristics (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. Feature selection provided six features, and based on them, classification was performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 for tree algorithm, Naïve Bayes, and penalized logistic regression, respectively. Fasting glucose, age, and body mass index together with features describing insulin action and secretion may predict the development of T2DM in women with a history of GDM
Insulin clearance is altered in women with a history of gestational diabetes progressing to type 2 diabetes
No full textBackground and aims: Insulin clearance is a relevant process in glucose homeostasis. In this observational study, we aimed to assess insulin clearance (ClINS) in women with former gestational diabetes (fGDM) both early after delivery and after a follow-up. Methods and results: We analysed 59 fGDM women, and 16 women not developing GDM (CNT). All women underwent an oral glucose tolerance test (OGTT) yearly, and an insulin-modified intravenous glucose tolerance test (IVGTT) at baseline and at follow-up end (until 7 years). Both IVGTT and OGTT ClINS was assessed as insulin secretion to plasma insulin ratio. We also defined IVGTT first (0–10 min) and second phase (10–180 min) ClINS. We found that 14 fGDM women progressed to type 2 diabetes (PROG), whereas 45 women remained diabetes-free (NONPROG). At baseline, IVGTT ClINS showed alterations in PROG, especially in second phase (0.88 ± 0.10 l·min−1 in PROG, 0.60 ± 0.06 in NONPROG, 0.54 ± 0.07 in CNT, p ≤ 0.03). Differences in ClINS were not found from OGTT. Cox regression analysis showed second phase ClINS as significant type 2 diabetes predictor (hazard ratio = 1.90, 95% confidence interval 1.09–3.30, p = 0.02). Conclusion: This study showed that insulin clearance derived from an insulin-modified IVGTT is notably altered in women with history of GDM progressing towards type 2 diabetes
Empirical Index for Easy Assessment of Pancreatic Beta-Cell Glucose Sensitivity During Pregnancy: A Machine Learning Approach
No full textBased on data measured during an oral glucose tolerance test, machine learning techniques were implemented to derive a simple empirical index for the estimation of the pancreatic beta-cell function in pregnant women, as assessed by mathematical modelling (beta-cell glucose sensitivity parameters). We studied a group of 84 pregnant women, who were analyzed by measuring and assessing a wide set of variables and parameters. Through a LASSO regularized support vector machine, we analyzed such wide batteries of variables/parameters and identified an index based on a simple algebraic equation (including glucose and C-peptide measurements only), which can predict the beta-cell glucose sensitivity with good accuracy (R2=0.64, p<0.0001, in the test set). In conclusion, the index is a good surrogate marker for the assessment of model-based beta-cell glucose sensitivity in pregnant women, thus it can be useful for easy application in the clinical context, where modelling analysis is not always possible
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Temporal Patterns of Glucagon and Its Relationships with Glucose and Insulin following Ingestion of Different Classes of Macronutrients
Full text linkBackground: glucagon secretion and inhibition should be mainly determined by glucose and insulin levels, but the relative relevance of each factor is not clarified, especially following ingestion of different macronutrients. We aimed to investigate the associations between plasma glucagon, glucose, and insulin after ingestion of single macronutrients or mixed-meal. Methods: thirty-six participants underwent four metabolic tests, based on administration of glucose, protein, fat, or mixed-meal. Glucagon, glucose, insulin, and C-peptide were measured at fasting and for 300 min following food ingestion. We analyzed relationships between time samples of glucagon, glucose, and insulin in each individual, as well as between suprabasal area-under-the-curve of the same variables (∆AUCGLUCA, ∆AUCGLU, ∆AUCINS ) over the whole participants’ cohort. Results: in individuals, time samples of glucagon and glucose were related in only 26 cases (18 direct, 8 inverse relationships), whereas relationship with insulin was more frequent (60 and 5, p < 0.0001). The frequency of significant relationships was different among tests, especially for direct relationships (p ≤ 0.006). In the whole cohort, ∆AUCGLUCA was weakly related to ∆AUCGLU (p ≤ 0.02), but not to ∆AUCINS, though basal insulin secretion emerged as possible covariate. Conclusions: glucose and insulin are not general and exclusive determinants of glucagon secretion/inhibition after mixed-meal or macronutrients ingestion
Insulin clearance in women with a history of gestational diabetes assessed by mathematical model analyses of intravenous glucose tolerance test
No full textCirculating concentrations of insulin are determined by a balance between the secretion rate of insulin from pancreatic beta-cells and insulin degradation (“clearance”). However, limited attention has been devoted to the study of insulin clearance in women with former gestational diabetes mellitus (GDM), which are known to be at increased type 2 diabetes risk. The aim of this study was to provide a detailed analysis of insulin clearance in women with former GDM. A population of 156 white Caucasian women, was analyzed early postpartum (4–6 months after delivery) and classified in two groups: women with previous GDM (pGDM, n = 115) and women that remain healthy during pregnancy (CNT, n = 41). All women underwent a 3-hour Insulin-Modified Intravenous Glucose Tolerance Test (IM-IVGTT). Insulin clearance temporal patterns were derived by mathematical modelling of IM-IVGTT data; average insulin clearance values were also considered during the whole test, and in the first - (0–10 min) and second phase (10–180 min). Insulin clearance temporal patterns were found to be different between CNT and pGDM group (p < 0.0001). Average insulin clearance was found different over the second phase of the test (p = 0.04), being equal to 0.54 [0.41] and 0.59 [0.41] l·min−1 in CNT and pGDM group, respectively. In conclusion, some abnormalities in former GDM women, compared to a group of healthy women were detected. This may be of relevance for more accurate estimation of type 2 diabetes risk
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