Type 2 Diabetes and Associated Diseases

Mats Karlsson, Maria Kjellsson

Around 6-10% of the world population is estimated to suffer from diabetes[1] (depending on region) and since the rule of halves framework applies to diabetes, only a minor part of these patients lives healthy lives; half of the 422 million people with type 2 Diabetes are not diagnosed; half of those diagnosed do not receive care; half of the cared for patients do not achieve treatment target; and half of the patients achieving target do not achieve desirable outcomes[2]. There are many challenges to be tackled in prevention, diagnosis, treatment availability and optimisation and protection against long-term complications, e.g. cardiovascular disease and kidney failure.

The first mathematical model in the diabetic field was published in 1961 by Bolie[3]; a differential equation system describing the glucose-insulin dynamic. Since then mathematical models have been used to understand and predict the complex aspects of diabetes, e.g. glucose homeostasis for diagnosis and insulin pumps, epidemiology of diabetes and its complications and cost-effectiveness of diabetes care.

Pharmacometric models is used to support drug development in diabetes. Pharmacometric models have been used to understand pharmacokinetics of anti-hyperglycaemic drugs and pharmacodynamics of short- to medium-term biomarkers, e.g. FPG, HbA1c, i.e. treatment optimisation. In recent years, focus of pharmacometric aided-drug development has shifted towards prevention with models of impaired glucose tolerance and obesity with associated progression, as well as protection against and treatment of complications.

The research in our group have followed a similar trend as that of the pharmaceutical industry and we have developed models describing pharmacokinetics[4], glucose homeostasis[5],[6],[7],[8],[9],[10],[11],[12] and time-course of HbA1c[13],[14],[15], investigated design of studies with short- and medium-term biomarkers[16],[17],[18],[19], and quantified disease progression in diabetes[20] and diabetes onset[21],[22]. In recent years, we have focused more on factors predictive of onset e.g. obesity[23],[24],[25], and smoking[26],[27], and complications of diabetes, e.g. cardiovascular disease and decreased kidney function[28] and overall survival[29].

In 2018, the group received a grant from the Swedish Research Council to develop a tool, CARE (Cardiovascular Absolute Risk Estimator). This tool will, based on an individual’s laboratory values and weight, visualise the absolute risk of cardiovascular disease. Prognostic predictions of how the risk change for various treatments and success in reaching treatment target, will hopefully motivate patient adherence to therapy as well as help engage the patient in treatment decisions.

Mats Karlsson, Maria Kjellsson


References:

[2] Hart JT. Rule of halves: implications of increasing diagnosis and reduced dropout from future workload and prescribing costs in primary care. Br J Gen Pract. 1992; 42: 116-119.

[3] Bolie VW. Coefficients of normal blood glucose regulation. J Appl Physiol. 1961; 16: 783-788.

[4] Stage TB, Wellhagen G, Christensen MMH, Guiastrennec B, Brøsen K, Kjellsson MC. Using a semi-mechanistic model to identify the main sources of cariability of metformin pharmacokinetics. Basic Clin Pharmacol Toxicol. 2019; 124: 105-114.

[5] Choy S, Hénin E, van der Walt JS, Kjellsson MC, Karlsson MO. Identification of the primary mechanism of action of an insulin secretagogue from meal test data in healthy colunteers based on an integrated glucose-insulin model. J Pharmacokinet Pharmacodyn. 2013; 40: 1-10.

[6] Røge RM, Klim S, Kristensen NR, Ingwersen SH, Kjellsson MC. Modeling of 24-hours glucose and insulin profiles in patients with type 2 diabetes mellitus treated with biphasic insulin aspart. J Clin Pharmacol. 2014; 54: 809-817.

[7] Alskär O, Bagger JI, Røge RM, Knop FK, Karlsson MO, Vilsbøll T, Kjellsson MC. Semimechanistic modelling describing gastric emptying and glucose absorption in healthy subjects and patients with type 2 diabetes. J Clin Pharmacol. 2016; 56: 340-348.

[8] Røge RM, Klim S, Ingwersen SH, Kjellsson MC, Kristensen NR. The effect of a GLP-1 analog on glucose homeostasis in type 2 diabetes mellitus quantified by and integrated glucose insulin model. CPT Pharmacometrics Syst Pharmacol. 2015; 4: e00011.

[9] Parkinson J, Hamrén B, Kjellsson MC, Skrtic S. Application of the integrated glucose-insulin model for cross-study characterization of T2DM patients on metformin background treatement. Br J Clin Pharmacol. 2016; 82: 1613-1624.

[10] Ibrahim MMA, Largajolli A, Karlsson MO, Kjellsson MC. The integrated glucose insulin minimal model: an improved version. Eur J Pharma Sci. 2019; 134: 7-19.

[11] Røge RM, Bagger JI, Alskär O, Kristensen NR, Klim S, Holst JJ, Ingwersen SH, Karlsson MO, Knop FK, Vilsbøll T, Kjellsson MC. Mathematical modelling of glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 following ingestion of glucose. Basiv Clin Pharmacol Toxicol. 2017; 121: 290-297.

[12] Alskär O, Karlsson MO, Kjellsson MC. Model-based interspecies scaling of glucose homeostasis. CPT Pharmacometrics Syst Pharmacol. 2017; 6: 778-786.

[13] Møller JB, Overgaard RV, Kjellsson MC, Kristensen NR, Klim S, Ingwersen SH, Karlsson MO. Longitudinal modeling of the relationship between mean plasma glucose and HbA1c following antidiabetic treatment. CPT Pharmacometrics Syst Pharmacol. 2013; 2: e82.

[14] Møller JB, Kristensen NR, Klim S, Karlsson MO, Ingwersen SH, Kjellsson MC. Methods for predicting diabetes phase III efficacy outcome from early data: superior performance obtained using longitudinal approach. CPT Pharmacometrics Syst Pharmacol. 2014; 3: e122.

[15] Claussen A, Møller JB, Kristensen NR, Klim S, Kjellsson MC, Ingwersen SH, Karlsson MO. Impact of demographics and disease progression on the relationship between glucose and HbA1c. Eur J Pharm Sci. 2017; 104: 417-423.

[16] Kjellsson MC, Cosson VF, Mazer NA, Frey N, Karlsson MO. A model-based approach to predict longitudinal Hba1c, using early phase glucose data from type 2 diabetes mellitus patients after anti-diabetic treatment. J Clin Pharmacol. 2013; 53: 589-600.

[17] Wellhagen GJ, Karlsson MO, Kjellsson MC. Comparison of power, prognosis, and extrapolation properties of four population pharmacodynamic models of HbA1c in type 2 diabetes. CPT Pharmacometrics Syst Pharmacol. 2018; 7: 331-341.

[18] Ibrahim MMA, Ghadzi SMS, Kjellsson MC, Karlsson MO. Study design selection in early clinical anti-hyperglycemic drug development: a simulation study of glucose toelrance tests. CPT Pharmacometrics Syst Pharmacol. 2018; 7: 432-441.

[19] Sheikh Ghadzi SM, Karlsson MO. Kjellsson MC. Implications for drug characterization in glucose tolerance tests without insulin: simulation study of power and predictions using model-based analysis. CPT Pharmacometrics Syst Pharmacol. 2017; 6: 686-694.

[20] Choy S, Kjellsson MC, Karlsson MO, de Winter W. Weight-HbA1c-insulin-glucose model for describing progression of type 2 diabetes. CPT Pharmacometrics Syst Pharmacol. 2016; 5: 11-19.

[21] Choy S, de Winter W, Karlsson MO, Kjellsson MC. Modeling the disease progression from healthy to overt diabetes in ZDSD rats. AAPS J. 2016; 18: 1203-1212.

[22] Ibrahim MMA, de Melo VD, Uusitupa M, Tuomilehto J, Lindström J, Kjellsson MC, Karlsson MO. Competing risks analysis of the Finnish diabetes prevention study. PAGE 28. 2019; abstr 9033.

[23] Leohr J, Heathman M, Kjellsson MC. Semi-Physiological model of postprandial triglyceride response in lean, obese and very obese individuals after a high-fat meal. Diabetes Obes Metab. 2018; 20: 660-666.

[24] Leohr J, Heathman M, Kjellsson MC. A semi-physiological model of postprandial triglyceride response following anti-obesity therapy. PAGE 26. 2017; abstr 7227.

[25] Leohr J, Kjellsson MC. A categorical model of sweet/fat preference taste in lean, obsess and very obese subjects. PAGE 27. 2018; abstr 8521.

[26] Germovsek E, Hansson A, Kjellsson MC, Perez Ruixo JJ, Westing Å, Soons AP, Vermeulen A, Karlsson MO. Relating nicotine plasma concentration to momentary craving across four nicotine replacement therapy formulations. Clin Pharmacol Ther. 2019; epub head of print.

[27] Germovsek E, Hansson A, Karlsson MO, Westin Å, Soons PA, Vermeulen A, Kjellsson MC. A time-to-event model relating integrated craving to risk of smoking relapse across different nicotine replacement therapy formulations. PAGE 28. 2019; abstr 9074.

[28] Wellhagen G, Hamrén B, Kjellsson MC, Åstrand M. Modeliing UACR as a clinical endpoint. PAGE 28. 2019; abstr 9152.

[29] Kunina H, Kjellsson MC. Diabetes progression modelling of competing risks of long-term complications and mortality using Swedish registry data. PAGE 28. 2019; abstr 9083.