Model-based analysis of postprandial glycemic response dynamics for different types of food

Background & aims Knowledge of postprandial glycemic response (PPGR) dynamics is important in nutrition management and diabetes research, care and (self)management. In daily life, food intake is the most important factor influencing the occurrence of hyperglycemia. However, the large variability in PPGR dynamics to different types of food is inadequately predicted by existing glycemic measures. The objective of this study was therefore to quantitatively describe PPGR dynamics using a systems approach. Methods Postprandial glucose and insulin data were collected from literature for many different food products and mixed meals. The predictive value of existing measures, such as the Glycemic Index, was evaluated. A physiology-based dynamic model was used to reconstruct the full postprandial response profiles of both glucose and insulin simultaneously. Results We collected a large range of postprandial glucose and insulin dynamics for 53 common food products and mixed meals. Currently available glycemic measures were found to be inadequate to describe the heterogeneity in postprandial dynamics. By estimating model parameters from glucose and insulin data, the physiology-based dynamic model accurately describes the measured data whilst adhering to physiological constraints. Conclusions The physiology-based dynamic model provides a systematic framework to analyze postprandial glucose and insulin profiles. By changing parameter values the model can be adjusted to simulate impaired glucose tolerance and insulin resistance

DiaGame: Serious and personalized game for selfmanagement of diabetes

Patient gathered health data will be collected to optimize a personalized diabetes game, which we hypothesize will improve self management

DiaGame: Serious and personalized game for selfmanagement of diabetes

Patient gathered health data will be collected to optimize a personalized diabetes game, which we hypothesize will improve self management

CCDC 1517934: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures

Database for "The impact of amino acids on postprandial glucose and insulin kinetics in humans: a quantitative overview"

Different amino acids (AAs) may exert distinct effects on postprandial glucose and insulin concentrations. A quantitative comparison of the effects of AAs on glucose and insulin kinetics in humans is currently lacking. PubMed was queried to identify intervention studies reporting glucose and insulin concentrations after acute ingestion and/or intravenous infusion of AAs in healthy adults and those living with obesity and/or type 2 diabetes (T2DM). The systematic literature search identified 55 studies that examined the effects of L-leucine, L-isoleucine, L-alanine, L-glutamine, L-arginine, L-lysine, glycine, L-proline, L-phenylalanine, L-glutamate, branched-chain AAs (i.e., L-leucine, L-isoleucine, and L-valine), and multiple individual L-AAs on glucose and insulin concentrations. Oral ingestion of most individual AAs induced an insulin response but did not alter glucose concentrations in healthy participants. Specific AAs (i.e., leucine and isoleucine) co-ingested with glucose exerted a synergistic effect on the postprandial insulin response and attenuated the glucose response compared to glucose intake alone in healthy participants. Oral AA ingestion as well as intravenous AA infusion was able to stimulate an insulin response and decrease glucose concentrations in T2DM and obese individuals. The extracted information is publicly available and can serve multiple purposes such as computational modeling

Computational modelling of energy balance in individuals with Metabolic Syndrome

Abstract Background A positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expenditure and energy balance in a large set of individual model realizations. Results We developed and applied a strategy to select specific individual models for a detailed analysis of heterogeneity in energy metabolism. Models were stratified based on energy expenditure. A substantial surplus of energy was found to be present during MetS development, which explains the weight gain during MetS development. In the majority of the models, energy was mainly expended in the peripheral tissues, but also distinctly different subgroups were identified. In silico perturbation of the system to induce increased peripheral energy expenditure implied changes in lipid metabolism, but not in carbohydrate metabolism. In silico analysis provided predictions for which individual models increase of peripheral energy expenditure would be an effective treatment. Conclusion The computational analysis confirmed that the energy imbalance plays an important role in the development of obesity. Furthermore, the model is capable to predict whether an increase in peripheral energy expenditure â for instance by cold exposure to activate brown adipose tissue (BAT) â could resolve MetS symptoms

Data and code for "A microfluidics platform for combinatorial drug screening on cancer biopsies"

Version of the code and data used in the paper. Corresponding Github repository https://github.com/saezlab/BraDiPlu

Characterization of disease-specific cellular abundance profiles of chronic inflammatory skin conditions from deconvolution of biopsy samples

Abstract Background Psoriasis and atopic dermatitis are two inflammatory skin diseases with a high prevalence and a significant burden on the patients. Underlying molecular mechanisms include chronic inflammation and abnormal proliferation. However, the cell types contributing to these molecular mechanisms are much less understood. Recently, deconvolution methodologies have allowed the digital quantification of cell types in bulk tissue based on mRNA expression data from biopsies. Using these methods to study the cellular composition of the skin enables the rapid enumeration of multiple cell types, providing insight into the numerical changes of cell types associated with chronic inflammatory skin conditions. Here, we use deconvolution to enumerate the cellular composition of the skin and estimate changes related to onset, progress, and treatment of these skin diseases. Methods A novel signature matrix, i.e. DerM22, containing expression data from 22 reference cell types, is used, in combination with the CIBERSORT algorithm, to identify and quantify the cellular subsets within whole skin biopsy samples. We apply the approach to public microarray mRNA expression data from the skin layers and 648 samples from healthy subjects and patients with psoriasis or atopic dermatitis. The methodology is validated by comparison to experimental results from flow cytometry and immunohistochemistry studies, and the deconvolution of independent data from isolated cell types. Results We derived the relative abundance of cell types from healthy, lesional, and non-lesional skin and observed a marked increase in the abundance of keratinocytes and leukocytes in the lesions of both inflammatory dermatological conditions. The relative fraction of these cells varied from healthy to diseased skin and from non-lesional to lesional skin. We show that changes in the relative abundance of skin-related cell types can be used to distinguish between mild and severe cases of psoriasis and atopic dermatitis, and trace the effect of treatment. Conclusions Our analysis demonstrates the value of this new resource in interpreting skin-derived transcriptomics data by enabling the direct quantification of cell types in a skin sample and the characterization of pathological changes in tissue composition

Additional file 1: of Characterization of disease-specific cellular abundance profiles of chronic inflammatory skin conditions from deconvolution of biopsy samples

Table S2. Signature matrix DerM22. (XLSX 654 kb