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Semi-mechanistic models of glucose homeostasis and disease progression in type 2 diabetes
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics Group)
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by consistently high blood glucose, resulting from a combination of insulin resistance and reduced capacity of β-cells to secret insulin. While the exact causes of T2DM is yet unknown, obesity is known to be a major risk factor as well as co-morbidity for T2DM. As the global prevalence of obesity continues to increase, the association between obesity and T2DM warrants further study. Traditionally, mathematical models to study T2DM were mostly empirical and thus fail to capture the dynamic relationship between glucose and insulin. More recently, mechanism-based population models to describe glucose-insulin homeostasis with a physiological basis were proposed and offered a substantial improvement over existing empirical models in terms of predictive ability.

The primary objectives of this thesis are (i) examining the predictive usefulness of semi-mechanistic models in T2DM by applying an existing population model to clinical data, and (ii) exploring the relationship between obesity and T2DM and describe it mathematically in a novel semi-mechanistic model to explain changes to the glucose-insulin homeostasis and disease progression of T2DM.

Through the use of non-linear mixed effects modelling, the primary mechanism of action of an antidiabetic drug has been correctly identified using the integrated glucose-insulin model, reinforcing the predictive potential of semi-mechanistic models in T2DM. A novel semi-mechanistic model has been developed that incorporated a relationship between weight change and insulin sensitivity to describe glucose, insulin and glycated hemoglobin simultaneously in a clinical setting. This model was also successfully adapted in a pre-clinical setting and was able to describe the pathogenesis of T2DM in rats, transitioning from healthy to severely diabetic.

This work has shown that a previously unutilized biomarker was found to be significant in affecting glucose homeostasis and disease progression in T2DM, and that pharmacometric models accounting for the effects of obesity in T2DM would offer a more complete physiological understanding of the disease.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. , 78 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 210
Keyword [en]
pharmacokinetics, pharmacodynamics, pharmacometrics, glucose homeostasis, insulin, type 2 diabetes, obesity, weight, visceral adipose tissue, HbA1c, non-linear mixed effects, modelling, disease progression, ZDSD rats
National Category
Endocrinology and Diabetes
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-273709ISBN: 978-91-554-9456-8 (print)OAI: oai:DiVA.org:uu-273709DiVA: diva2:894953
Public defence
2016-03-04, B41, Biomedicinskt Centrum (BMC), Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2016-02-05 Created: 2016-01-17 Last updated: 2016-02-12
List of papers
1. Identification of the primary mechanism of action of an insulin secretagogue from meal test data in healthy volunteers based on an integrated glucose-insulin model
Open this publication in new window or tab >>Identification of the primary mechanism of action of an insulin secretagogue from meal test data in healthy volunteers based on an integrated glucose-insulin model
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2013 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no 1, 1-10 p.Article in journal (Refereed) Published
Abstract [en]

The integrated glucose–insulin (IGI) model is a previously developed semi-mechanistic model that incorporates control mechanisms for the regulation of glucose production, insulin secretion, and glucose uptake. It has been shown to adequately describe insulin and glucose profiles in both type 2 diabetics and healthy volunteers following various glucose tolerance tests. The aim of this study was to investigate the ability of the IGI model to correctly identify the primary mechanism of action of glibenclamide (Gb), based on meal tolerance test (MTT) data in healthy volunteers. IGI models with different mechanism of drug action were applied to data from eight healthy volunteers participating in a randomized crossover study with five single-dose tests (placebo and four drug arms). The study participants were given 3.5 mg of Gb, intravenously or orally, or 3.5 mg of the two main metabolites M1 and M2 intravenously, 0.5 h prior to a standardized breakfast with energy content of 1800 kJ. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed using NONMEM®. Drug effects that increased insulin secretion resulted in the best model fit, thus identifying the primary mechanism of action of Gb and metabolites as insulin secretagogues. The model also quantified the combined effect of Gb, M1 and M2 to have a fourfold maximal increase on endogenous insulin secretion, with an EC50 of 169.1 ng mL−1 for Gb, 151.4 ng mL−1 for M1 and 267.1 ng mL−1 for M2. The semi-mechanistic IGI model was successfully applied to MTT data and identified the primary mechanism of action for Gb, quantifying its effects on glucose and insulin time profiles.

Place, publisher, year, edition, pages
Springer, 2013
Keyword
Glibenclamide, Semi-mechanistic, Meal tolerance test, Integrated glucose–insulin model, NONMEM
National Category
Endocrinology and Diabetes
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-187779 (URN)10.1007/s10928-012-9281-1 (DOI)000313955700001 ()23179858 (PubMedID)
Available from: 2012-12-10 Created: 2012-12-10 Last updated: 2017-12-07Bibliographically approved
2. Weight-HbA1c-Insulin-Glucose Model for Describing Disease Progression of Type 2 Diabetes
Open this publication in new window or tab >>Weight-HbA1c-Insulin-Glucose Model for Describing Disease Progression of Type 2 Diabetes
2016 (English)In: CPT: Pharmacometrics & Systems Pharmacology, ISSN 2163-8306, Vol. 5, no 1, 11-19 p.Article in journal (Refereed) Published
Abstract [en]

A previous semi-mechanistic model described changes in fasting serum insulin (FSI), fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c) in patients with type 2 diabetic mellitus (T2DM) by modeling insulin sensitivity and β-cell function. It was later suggested that change in body weight could affect insulin sensitivity, which this study evaluated in a population model to describe the disease progression of T2DM. Nonlinear mixed effects modeling was performed on data from 181 obese patients with newly diagnosed T2DM managed with diet and exercise for 67 weeks. Baseline β-cell function and insulin sensitivity were 61% and 25% of normal, respectively. Management with diet and exercise (mean change in body weight = -4.1 kg) was associated with an increase of insulin sensitivity (30.1%) at the end of the study. Changes in insulin sensitivity were associated with a decrease of FPG (range, 7.8–7.3 mmol/L) and HbA1c (6.7–6.4%). Weight change as an effector on insulin sensitivity was successfully evaluated in a semi-mechanistic population model.

Keyword
diabetes, disease progression, semi-mechanistic, population model, weight, glucose, insulin
National Category
Endocrinology and Diabetes
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-272228 (URN)10.1002/psp4.12051 (DOI)000381560300002 ()26844011 (PubMedID)
Available from: 2016-01-12 Created: 2016-01-12 Last updated: 2016-10-06Bibliographically approved
3. Modelling the effect of very low calorie diet on weight and fasting plasma glucose in obese type 2 diabetic patients
Open this publication in new window or tab >>Modelling the effect of very low calorie diet on weight and fasting plasma glucose in obese type 2 diabetic patients
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background: Change in weight (WT) as a result of diet changes is closely associated with change in fasting plasma glucose (FPG) in type 2 diabetes mellitus (T2DM) patients. Two hypotheses on this relationship are 1) weight loss induces a change in an intermediary effector that reduces FPG, with the intermediary effector being insulin sensitivity (IS) or 2) an underlying change of the system affects weight as well as FPG. The aim of this study was to test these hypotheses using non-linear mixed effects modelling on summary level data from publications of weight loss with very low calorie diets (VLCD).

Methods: Summary level data was gathered from 8 clinical studies of diabetic patients (n=167 from 12 arms) treated with VLCD where weight and FPG was measured. The patients had a baseline weight ranging from 93-118 kg, and baseline FPG ranging from 91-321 mg/dL, treated with VLCD for up to 224 days. Non-linear mixed-effects modelling was performed using NONMEM 7.2.

Results: Both weight and FPG was modelled using indirect response models, with VLCD implemented as an instantaneous inhibitory effect on the input. The objective function value for the model describing hypothesis 2 was significantly lower than for hypothesis 1. The VLCD diet was estimated to reduce 42% of the Kin of weight and 51% of the Kin of FPG. Baseline BMI was a significant covariate effect on the scaling factor for the effect on FPG.

Conclusions: The model with an underlying mechanism that affects both weight and FPG was found to better describe the data than using weight loss as an effector on a mediator through which FPG is reduced.

Keyword
Body weight, fasting plasma glucose, type 2 diabetes, very low calorie diet, modelling, weight loss, meta-analysis
National Category
Endocrinology and Diabetes
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-272229 (URN)
Available from: 2016-01-12 Created: 2016-01-12 Last updated: 2016-02-08
4. Modelling the disease progression from healthy to overt diabetes in ZDSD rats
Open this publication in new window or tab >>Modelling the disease progression from healthy to overt diabetes in ZDSD rats
(English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416Article in journal (Other academic) Submitted
Abstract [en]

Introduction: Studying the critical transitional phase between healthy to overtly diabetic in type 2 diabetes mellitus (T2DM) is of interest, but acquiring such clinical data is impractical due to ethical concerns and the long study duration required. ZDSD rats are a strain of rats bred specifically to spontaneously develop T2DM, and a population model using ZDSD rats was developed to describe this transition through altering insulin sensitivity (IS) as a result of accumulating excess body weight and β-cell function (BCF) to affect glucose-insulin homeostasis.

Methods and Materials: Body weight, fasting plasma glucose (FPG), and fasting serum insulin (FSI) were collected over 24 weeks from ZDSD rats (n=23) at age 7 weeks. A semi-mechanistic model previously developed with clinical data was adapted to rat data with BCF and IS estimated relative to humans. Non-linear mixed-effect model estimation was performed using NONMEM 7.3 with first-order interaction.

Results and Discussion: Baseline IS and BCF were 41% compared to healthy humans. BCF was described with a non-linear rise which peaked at 14 weeks before gradually declining to a negligible level. A component for excess growth reflecting obesity was used to affect IS, and a FPG-dependent urine effect exerted a 2 to 6-fold increase on the elimination of FPG.

Conclusion:  A semi-mechanistic model to describe the dynamics of glucose and insulin was successfully developed for a rat population, transitioning from healthy to advanced diabetes. It is also shown that weight loss can be modeled to mimic the “starvation in the midst of plenty” phenomenon seen in advanced hyperglycemia.

Keyword
diabetes, disease progression, population model, rats, glucose, insulin, weight, insulin, glucose, Beta-cell function
National Category
Endocrinology and Diabetes
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-272230 (URN)
Available from: 2016-01-12 Created: 2016-01-12 Last updated: 2017-11-30

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