Jonas B. Møller

Body composition is the main determinant for the difference in type 2 diabetes pathophysiology between Japanese and Caucasians.

OBJECTIVE This cross-sectional clinical study compared the pathophysiology of type 2 diabetes in Japanese and Caucasians and investigated the role of demographic, genetic, and lifestyle-related risk factors for insulin resistance and β-cell response. RESEARCH DESIGN AND METHODS A total of 120 Japanese and 150 Caucasians were enrolled to obtain comparable distributions of high/low BMI values across glucose tolerance states (normal glucose tolerance, impaired glucose tolerance, and type 2 diabetes), which were assessed by oral glucose tolerance tests. BMI in the two cohorts was distributed around the two regional cutoff values for obesity. RESULTS Insulin sensitivity was higher in Japanese compared with Caucasians, as indicated by the homeostatic model assessment of insulin resistance and Matsuda indices, whereas β-cell response was higher in Caucasians, as measured by homeostatic model assessment of β-cell function, the insulinogenic indices, and insulin secretion ratios. Disposition indices were similar for Japanese and Caucasians at all glucose tolerance states, indicating similar β-cell response relative to the degree of insulin resistance. The main determinants for differences in metabolic indices were measures of body composition, such as BMI and distribution of adipose tissue. Differences in β-cell response between Japanese and Caucasians were not statistically significant following adjustment by differences in BMI. CONCLUSIONS Our study showed similar disposition indices in Japanese and Caucasians and that the major part of the differences in insulin sensitivity and β-cell response between Japanese and Caucasians can be explained by differences in body composition.

Ethnic Differences in Insulin Sensitivity, β-Cell Function, and Hepatic Extraction Between Japanese and Caucasians: A Minimal Model Analysis

CONTEXT Ethnic differences have previously been reported for type 2 diabetes. OBJECTIVE We aimed at assessing the potential differences between Caucasian and Japanese subjects ranging from normal glucose tolerance (NGT) to impaired glucose tolerance (IGT) and to type 2 diabetes. DESIGN This was a cross-sectional study with oral glucose tolerance tests to assess β-cell function, hepatic insulin extraction, and insulin sensitivity. PARTICIPANTS PARTICIPANTS included 120 Japanese and 150 Caucasian subjects. MAIN OUTCOMES Measures of β-cell function, hepatic extraction, and insulin sensitivity were assessed using C-peptide, glucose, and insulin minimal models. RESULTS Basal β-cell function (Φ(b)) was lower in Japanese compared with Caucasians (P < .01). In subjects with IGT, estimates of the dynamic (Φ(d)) and static (Φ(s)) β-cell responsiveness were significantly lower in the Japanese compared with Caucasians (P < .05). In contrast, values of insulin action showed higher sensitivity in the Japanese IGT subjects. Hepatic extraction was similar in NGT and IGT groups but higher in Japanese type 2 diabetic subjects (P < .01). Despite differences in insulin sensitivity, β-cell function, and hepatic extraction, the disposition indices were similar between the 2 ethnic groups at all glucose tolerance states. Furthermore, the overall insulin sensitivity and β-cell responsiveness for all glucose tolerance states were similar in Japanese and Caucasians after accounting for differences in body mass index. CONCLUSION Our study provides evidence for a similar ability of Japanese and Caucasians to compensate for increased insulin resistance.

Model Identification Using Stochastic Differential Equation Grey-Box Models in Diabetes

Background: The acceptance of virtual preclinical testing of control algorithms is growing and thus also the need for robust and reliable models. Models based on ordinary differential equations (ODEs) can rarely be validated with standard statistical tools. Stochastic differential equations (SDEs) offer the possibility of building models that can be validated statistically and that are capable of predicting not only a realistic trajectory, but also the uncertainty of the prediction. In an SDE, the prediction error is split into two noise terms. This separation ensures that the errors are uncorrelated and provides the possibility to pinpoint model deficiencies. Methods: An identifiable model of the glucoregulatory system in a type 1 diabetes mellitus (T1DM) patient is used as the basis for development of a stochastic-differential-equation-based grey-box model (SDE-GB). The parameters are estimated on clinical data from four T1DM patients. The optimal SDE-GB is determined from likelihood-ratio tests. Finally, parameter tracking is used to track the variation in the “time to peak of meal response” parameter. Results: We found that the transformation of the ODE model into an SDE-GB resulted in a significant improvement in the prediction and uncorrelated errors. Tracking of the “peak time of meal absorption” parameter showed that the absorption rate varied according to meal type. Conclusion: This study shows the potential of using SDE-GBs in diabetes modeling. Improved model predictions were obtained due to the separation of the prediction error. SDE-GBs offer a solid framework for using statistical tools for model validation and model development.

Longitudinal Modeling of the Relationship Between Mean Plasma Glucose and HbA1c Following Antidiabetic Treatments

Late‐phase clinical trials within diabetes generally have a duration of 12–24 weeks, where 12 weeks may be too short to reach steady‐state glycated hemoglobin (HbA1c). The main determinant for HbA1c is blood glucose, which reaches steady state much sooner. In spite of this, few publications have used individual data to assess the time course of both glucose and HbA1c, for predicting HbA1c. In this paper, we present an approach for predicting HbA1c at end‐of‐trial (24–28 weeks) using glucose and HbA1c measurements up to 12 weeks. The approach was evaluated using data from 4 trials covering 12 treatment arms (oral antidiabetic drug, glucagon‐like peptide‐1, and insulin treatment) with measurements at 24–28 weeks to evaluate predictions vs. observations. HbA1c percentage was predicted for each arm at end‐of‐trial with a mean prediction error of 0.14% [0.01;0.24]. Furthermore, end points in terms of HbA1c reductions relative to comparator were accurately predicted. The proposed model provides a good basis to optimize late‐stage clinical development within diabetes.

Chronic kidney disease in patients with the Philadelphia-negative chronic myeloproliferative neoplasms.

BACKGROUND The progression of kidney function and frequency of chronic kidney disease (CKD) in patients with the Philadelphia-negative myeloproliferative neoplasms (MPN) is unknown, although CKD is linked to increased mortality. METHODS This longitudinal retrospective study evaluates the estimated glomerular filtration rate (eGFR) in 143 MPN patients over a period of 9 years. RESULTS 29% of patients had CKD stage 3 or 4 at time of diagnosis. 20% of patients had a rapid annual loss of eGFR (>3mL/min/1.73m(2)) and eGFR was negatively correlated to monocyte and neutrophil counts. CONCLUSION Kidney impairment might contribute to the increased mortality observed in MPN patients.

Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches

The link between glucose and HbA1c at steady state has previously been described using steady‐state or longitudinal relationships. We evaluated five published methods for prediction of HbA1c after 26/28 weeks using data from four clinical trials. Methods (1) and (2): steady‐state regression of HbA1c on fasting plasma glucose and mean plasma glucose, respectively, (3) an indirect response model of fasting plasma glucose effects on HbA1c, (4) model of glycosylation of red blood cells, and (5) coupled indirect response model for mean plasma glucose and HbA1c. Absolute mean prediction errors were 0.61, 0.38, 0.55, 0.37, and 0.15% points, respectively, for Methods 1 through 5. This indicates that predictions improved by using mean plasma glucose instead of fasting plasma glucose, by inclusion of longitudinal glucose data and further by inclusion of longitudinal HbA1c data until 12 weeks. For prediction of trial outcome, the longitudinal models based on mean plasma glucose (Methods 4 and 5) had substantially better performance compared with the other methods.

Impact of demographics and disease progression on the relationship between glucose and HbA1c

Context Several studies have shown that the relationship between mean plasma glucose (MPG) and glycated haemoglobin (HbA1c) may vary across populations. Especially race has previously been referred to shift the regression line that links MPG to HbA1c at steady‐state (Herman & Cohen, 2012). Objective To assess the influence of demographic and disease progression‐related covariates on the intercept of the estimated linear MPG‐HbA1c relationship in a longitudinal model. Data Longitudinal patient‐level data from 16 late‐phase trials in type 2 diabetes with a total of 8927 subjects was used to study covariates for the relationship between MPG and HbA1c. The analysed covariates included age group, BMI, gender, race, diabetes duration, and pre‐trial treatment. Differences between trials were taken into account by estimating a trial‐to‐trial variability component. Participants Participants included 47% females and 20% above 65 years. 77% were Caucasian, 9% were Asian, 5% were Black and the remaining 9% were analysed together as other races. Analysis Estimates of the change in the intercept of the MPG‐HbA1c relationship due to the mentioned covariates were determined using a longitudinal model. Results The analysis showed that pre‐trial treatment with insulin had the most pronounced impact associated with a 0.34% higher HbA1c at a given MPG. However, race, diabetes duration and age group also had an impact on the MPG‐HbA1c relationship. Conclusion Our analysis shows that the relationship between MPG and HbA1c is relatively insensitive to covariates, but shows small variations across populations, which may be relevant to take into account when predicting HbA1c response based on MPG measurements in clinical trials. Graphical abstract Forest plot showing model estimates for the change in HbA1c at steady state at any given MPG for each covariate compared to a reference value (with point estimate and 95% confidence interval). Figure. No Caption available.