Weight-HbA1c-insulin-glucose model for describing disease progression of type 2 diabetes

Time-restricted eating: Watching the clock to treat obesity

Time-restricted eating (TRE) has become a popular strategy to treat obesity. TRE involves confining the eating window to 4-10 h per day and fasting for the remaining hours (14-20 h fast). During the eating window, individuals are not required to monitor food intake. The sudden rise in popularity of TRE is most likely due to its simplicity and the fact that it does not require individuals to count calories to lose weight. This feature of TRE may appeal to certain individuals with obesity, and this could help produce lasting metabolic health improvements. The purpose of this review is to summarize current evidence from randomized clinical trials of TRE (without calorie counting) on body weight and metabolic risk factors. The efficacy of TRE in various populations groups, including those with obesity, type 2 diabetes (T2DM), and polycystic ovary syndrome (PCOS), is also examined.

Efficacy and safety of empagliflozin in combination with insulin in Chinese patients with type 2 diabetes and insufficient glycaemic control: A phase III, randomized, double-blind, placebo-controlled, parallel study

AIM

To evaluate the efficacy and safety of empagliflozin in combination with insulin ± oral antidiabetic drugs (OADs) over 24 weeks, in Chinese patients with type 2 diabetes (T2D) who had insufficient glycaemic control.

MATERIALS AND METHODS

This was a randomized, double-blind, placebo-controlled, parallel group, multicentre phase III study. Adult patients with T2D and insufficient glycaemic control who received insulin ± up to two OADs were randomized (1:1:1) to receive empagliflozin 10 or 25 mg, or placebo for 24 weeks. The primary endpoint was change from baseline in HbA1c at week 24.

RESULTS

Of 219 randomized patients, 73 patients were in each treatment group; baseline characteristics were comparable among the groups. There was a significantly larger decrease from baseline in HbA1c (adjusted mean treatment difference -0.99 and -0.98 for in the empagliflozin 10 and 25 mg groups, respectively; P < .0001) with both doses of empagliflozin than with placebo. There were also significantly larger decreases from baseline in fasting plasma glucose, 2-hour postprandial glucose and body weight with both empagliflozin doses than with placebo. Among patients in the empagliflozin 10 mg, 25 mg and placebo groups, 17.8%, 9.6% and 11.0% reported confirmed hypoglycaemic events, respectively (nominal P = .2422 and .7661 in the empagliflozin 10 and 25 mg groups, respectively), and no Clinical Events Committee-confirmed diabetic ketoacidosis events were reported.

CONCLUSIONS

In Chinese patients with T2D, empagliflozin combined with insulin ± OADs improved glycaemic control and was well tolerated, without an increased risk of hypoglycaemia.

Ethanol extract of the mushroom Coprinus comatus exhibits antidiabetic and antioxidant activities in streptozotocin-induced diabetic rats

CONTEXT

Edible mushrooms have a long history of use in traditional Chinese or Japanese medicine. Coprinus comatus (O.F. Müll.) Pers. (Agaricaceae) contains antioxidant and antidiabetic agents.

OBJECTIVE

To identify the benefits of ethanol extracts of the C. comatus fruit body in streptozotocin-induced hyperglycaemic rats by evaluating their blood glucose, glycosylated haemoglobin (HbA1c), insulin, glucagon-like peptide-1 (GLP-1), dipeptidyl peptidase-4 (DPP-4), and glutathione (GSH) levels, with and without extract administration.

MATERIALS AND METHODS

Wistar rats were either left untreated or were administered 45 mg/kg body weight (BW) streptozotocin; 45 mg/kg BW metformin; or 250, 500, or 750 mg/kg BW extract for 14 days. The blood glucose, GLP-1, DPP-4, GSH, insulin, and HbA1c levels were determined. Data were analysed using analysis of variance and Duncan's multiple range tests.

RESULTS

Preliminary data showed that administration of C. comatus ethanol extract dose of 250, 500, and 750 mg orally has no toxicity effects after 24 h administration. The ethanolic extract of fruiting body of C. comatus considerably reduced the rat's fasting blood glucose levels 26.69%, and DPP-4 6.97% at dose of 750 mg. The extract reduced HbA1c 4-4.30%, increased GLP-1 71.09%, GSH 11.19% at dose of 500 mg, and increased insulin levels 13.83%. Extracts contain bioactive compounds such as flavonoid, alkaloid, terpenoids, vitamins C and E, rutin, and saponin.

CONCLUSIONS

The C. comatus extract can be used as herbal medicine that reduces diabetic symptoms. Further investigation on C. comatus extracts should be conducted with gas chromatography-mass spectrometry to characterise the bioactive compounds.

Optimization of trial duration to predict long-term HbA1c change with therapy: A pharmacometrics simulation-based evaluation

Glycated hemoglobin (HbA1c) is the main biomarker of diabetes drug development. However, because of its delayed turnover, trial duration is rarely shorter than 12 weeks, and being able to predict long-term HbA1c with precision using data from shorter studies would be beneficial. The feasibility of reducing study duration was therefore investigated in this study, assuming a model-based analysis. The aim was to investigate the predictive performance of 24- and 52-week extrapolations using data from up to 4, 6, 8 or 12 weeks, with six previously published pharmacometric models of HbA1c. Predictive performance was assessed through simulation-based dose-response predictions and model averaging (MA) with two hypothetical drugs. Results were consistent across the methods of assessment, with MA supporting the results derived from the model-based framework. The models using mean plasma glucose (MPG) or nonlinear fasting plasma glucose (FPG) effect, driving the HbA1c formation, showed good predictive performance despite a reduced study duration. The models, using the linear effect of FPG to drive the HbA1c formation, were sensitive to the limited amount of data in the shorter studies. The MA with bootstrap demonstrated strongly that a 4-week study duration is insufficient for precise predictions of all models. Our findings suggest that if data are analyzed with a pharmacometric model with MPG or FPG with a nonlinear effect to drive HbA1c formation, a study duration of 8 weeks is sufficient with maintained accuracy and precision of dose-response predictions.

Role of Disease Progression Models in Drug Development

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Characterisation of individual ferritin response in patients receiving chelation therapy

Aims

To develop a drug–disease model describing iron overload and its effect on ferritin response in patients affected by transfusion‐dependent haemoglobinopathies and investigate the contribution of interindividual differences in demographic and clinical factors on chelation therapy with deferiprone or deferasirox.

Methods

Individual and mean serum ferritin data were retrieved from 13 published studies in patients affected by haemoglobinopathies receiving deferiprone or deferasirox. A nonlinear mixed effects modelling approach was used to characterise iron homeostasis and serum ferritin production taking into account annual blood consumption, baseline demographic and clinical characteristics. The effect of chelation therapy was parameterised as an increase in the iron elimination rate. Internal and external validation procedures were used to assess model performance across different study populations.

Results

An indirect response model was identified, including baseline ferritin concentrations and annual blood consumption as covariates. The effect of chelation on iron elimination rate was characterised by a linear function, with different slopes for each drug (0.0109 [90% CI: 0.0079–0.0131] vs. 0.0013 [90% CI: 0.0008–0.0018] L/mg mo). In addition to drug‐specific differences in the magnitude of the ferritin response, simulation scenarios indicate that ferritin elimination rates depend on ferritin concentrations at baseline.

Conclusion

Modelling of serum ferritin following chronic blood transfusion enabled the evaluation of drug‐induced changes in iron elimination rate and ferritin production. The use of a semi‐mechanistic parameterisation allowed us to disentangle disease‐specific factors from drug‐specific properties. Despite comparable chelation mechanisms, deferiprone appears to have a significantly larger effect on the iron elimination rate than deferasirox.

Zucker Diabetic Sprague Dawley rat (ZDSD): type 2 diabetes translational research model

New Findings

What is the topic of this review?

The Zucker Diabetic‐Sprague Dawley (ZDSD) rat is in the early adoption phase of use by researchers in the fields of diabetes, including prediabetes, obesity and metabolic syndrome. It is essential that physiology researchers choose preclinical models that model human type 2 diabetes appropriately and are aware of the limitations on experimental design.

What advances does it highlight?

Our review of the scientific literature finds that although sex, age and diets contribute to variability, the ZDSD phenotype and disease progression model the characteristics of humans who have prediabetes and diabetes, including co‐morbidities.

Abstract

Type 2 diabetes (T2D) is a prevalent disease and a significant concern for global population health. For persons with T2D, clinical treatments target not only the characteristics of hyperglycaemia and insulin resistance, but also co‐morbidities, such as obesity, cardiovascular and renal disease, neuropathies and skeletal bone conditions. The Zucker Diabetic‐Sprague Dawley (ZDSD) rat is a rodent model developed for experimental studies of T2D. We reviewed the scientific literature to highlight the characteristics of T2D development and the associated phenotypes, such as metabolic syndrome, cardiovascular complications and bone and skeletal pathologies in ZDSD rats. We found that ZDSD phenotype characteristics are independent of leptin receptor signalling. The ZDSD rat develops prediabetes, then progresses to overt diabetes that is accelerated by introduction of a timed high‐fat diet. In male ZDSD rats, glycated haemoglobin (HbA1c) increases at a constant rate from 7 to >30 weeks of age. Diabetic ZDSD rats are moderately hypertensive compared with other rat strains. Diabetes in ZDSD rats leads to endothelial dysfunction in specific vasculatures, impaired wound healing, decreased systolic and diastolic cardiac function, neuropathy and nephropathy. Changes to bone composition and the skeleton increase the risk of bone fractures. Zucker Diabetic‐Sprague Dawley rats have not yet achieved widespread use by researchers. We highlight sex‐related differences in the ZDSD phenotype and gaps in knowledge for future studies. Overall, scientific data support the premise that the phenotype and disease progression in ZDSD rats models the characteristics in humans. We conclude that ZDSD rats are an advantageous model to advance understanding and discovery of treatments for T2D through preclinical research.

Cardiometabolic Benefits of Intermittent Fasting

This review aims to summarize the effects of intermittent fasting on markers of cardiometabolic health in humans. All forms of fasting reviewed here-alternate-day fasting (ADF), the 5:2 diet, and time-restricted eating (TRE)-produced mild to moderate weight loss (1-8% from baseline) and consistent reductions in energy intake (10-30% from baseline). These regimens may benefit cardiometabolic health by decreasing blood pressure, insulin resistance, and oxidative stress. Low-density lipoprotein cholesterol and triglyceride levels are also lowered, but findings are variable. Other health benefits, such as improved appetite regulation and favorable changes in the diversity of the gut microbiome, have also been demonstrated, but evidence for these effects is limited. Intermittent fasting is generally safe and does not result in energy level disturbances or increased disordered eating behaviors. In summary, intermittent fasting is a safe diet therapy that can produce clinically significant weight loss (>5%) and improve several markers of metabolic health in individuals with obesity.

Time restricted eating for the prevention of type 2 diabetes

Type 2 diabetes can potentially be prevented by targeted lifestyle and weight loss interventions. Time restricted eating (TRE) is a form of intermittent fasting that has emerged as a novel diet strategy to reduce body weight and improve glycaemic control. TRE involves eating within a certain window of time (usually 4 to 10 h), and water-fasting for the remaining hours of the day. The purpose of this review is to summarize the effects of TRE on body weight and markers of glycaemic control in human subjects. We also aim to provide mechanistic insights into the effect of TRE on insulin sensitivity and glucose tolerance. Results to date reveal that TRE produces mild weight loss (1%-4% from baseline) and energy restriction, when food consumption is restricted to 4-10 h/day. TRE also reduces fasting insulin and improves insulin sensitivity in individuals with prediabetes and those with obesity. Moreover, TRE improves glucose tolerance and decreases serum glucose excursions. The possible mechanisms underlying these benefits include increased autophagic flux, mild elevations in ketone bodies, a reduction in oxidative stress, and the stimulation of β-cell responsiveness. While these preliminary results offer promise for the use of TRE in the prevention of type 2 diabetes, larger and longer-term human trials will be needed to confirm these findings.

Impact of Weight Change in Adults with Type 2 Diabetes Mellitus: A Literature Review and Critical Analysis

Objective

Weight reduction is a key component of diabetes management in adults with type 2 diabetes mellitus (T2DM), yet the benefits of weight loss in T2DM patients have been difficult to quantify. We examined the medical literature regarding the relationships between weight change and 1) glycemic control and 2) cost and resource use.

Methods

Systematic searches were conducted in the electronic databases Embase, MEDLINE, and the Cochrane Database of Systematic Reviews to identify publications regarding the impact of weight change on T2DM outcomes from 2007 onward. Identified publications were screened for relevance against predefined eligibility criteria, and methodological approaches and results were extracted. Evidence for the impact of weight change on outcomes was evaluated and used to identify strengths, limitations, and gaps in the current literature.

Results

The number of studies meeting eligibility criteria for each outcome was: glycemic control (n=38) and cost and resource use (n=11). The relationship between weight change and glycemic control was dependent on the interplay of multiple factors, eg, the weight loss interventions employed, the antidiabetic medication classes used, the time horizon, and baseline BMI and glycemic control. With respect to cost and resource use, the review indicated that savings were associated with weight loss, and increased costs were associated with weight gain.

Conclusion

Studies regarding weight change in T2DM patients demonstrated varying effects on glycemic control and a positive association with costs and resource use, where weight loss was associated with decreased costs and resource use. Future studies may be able to clarify these relationships.

Type 2 diabetes: one disease, many pathways

Diabetes is a chronic, progressive disease that calls for longitudinal data and analysis. We introduce a longitudinal mathematical model that is capable of representing the metabolic state of an individual at any point in time during their progression from normal glucose tolerance to type 2 diabetes (T2D) over a period of years. As an application of the model, we account for the diversity of pathways typically followed, focusing on two extreme alternatives, one that goes through impaired fasting glucose (IFG) first and one that goes through impaired glucose tolerance (IGT) first. These two pathways are widely recognized to stem from distinct metabolic abnormalities in hepatic glucose production and peripheral glucose uptake, respectively. We confirm this but go beyond to show that IFG and IGT lie on a continuum ranging from high hepatic insulin resistance and low peripheral insulin resistance to low hepatic resistance and high peripheral resistance. We show that IFG generally incurs IGT and IGT generally incurs IFG on the way to T2D, highlighting the difference between innate and acquired defects and the need to assess patients early to determine their underlying primary impairment and appropriately target therapy. We also consider other mechanisms, showing that IFG can result from impaired insulin secretion, that non-insulin-dependent glucose uptake can also mediate or interact with these pathways, and that impaired incretin signaling can accelerate T2D progression. We consider whether hyperinsulinemia can cause insulin resistance in addition to being a response to it and suggest that this is a minor effect.

Pharmacodynamic Drug-Drug Interactions

Pharmacodynamic drug-drug interactions (DDIs) occur when the pharmacological effect of one drug is altered by that of another drug in a combination regimen. DDIs often are classified as synergistic, additive, or antagonistic in nature, albeit these terms are frequently misused. Within a complex pathophysiological system, the mechanism of interaction may occur at the same target or through alternate pathways. Quantitative evaluation of pharmacodynamic DDIs by employing modeling and simulation approaches is needed to identify and optimize safe and effective combination therapy regimens. This review investigates the opportunities and challenges in pharmacodynamic DDI studies and highlights examples of quantitative methods for evaluating pharmacodynamic DDIs, with a particular emphasis on the use of mechanism-based modeling and simulation in DDI studies. Advancements in both experimental and computational techniques will enable the application of better, model-informed assessments of pharmacodynamic DDIs in drug discovery, development, and therapeutics.

Characterization of changes in HbA1c in patients with and without secondary failure after metformin treatments by a population pharmacodynamic analysis using mixture models

The objective of the present study was to develop a population pharmacodynamic (PPD) model to describe the glycated hemoglobin (HbA1c)-lowering effects of metformin in type 2 diabetes mellitus patients with and without secondary failure and to characterize changes in HbA1c levels in the two subpopulations using a mixture model. Information on patients was collected retrospectively from electronic medical records. In this study, the mixture model was used to characterize the bimodal effects of metformin. A PPD analysis was performed using NONMEM 7.3.0. A physiological indirect response model, based on 829 HbA1c levels of 69 patients, described the time course for the HbA1c-lowering effects of metformin. Evidence for the different effectiveness of metformin subpopulations was provided using the mixture model. In the final PPD model, the inhibition effect was constant over a study duration in a patient subpopulation without secondary failure. In contrast, the inhibition effect decreased as a function of time after start of metformin treatment in a subpopulation with secondary failure. These results indicated that HbA1c improvements appeared to deteriorate over time in patients with secondary failure. In a PPD analysis of metformin, it was possible to assign patients with secondary failure using the mixture model.

The variability in beta-cell function in placebo-treated subjects with type 2 diabetes: application of the weight-HbA1c-insulin-glucose (WHIG) model

AIM

The weight-glycosylated haemoglobin (HbA1C)-insulin-glucose (WHIG) model describes the effects of changes in weight on insulin sensitivity (IS) in newly diagnosed, obese subjects receiving placebo treatment. This model was applied to a wider population of placebo-treated subjects, to investigate factors influencing the variability in IS and β-cell function.

METHODS

The WHIG model was applied to the WHIG dataset (Study 1) and two other placebo datasets (Studies 2 and 3). Studies 2 and 3 consisted of nonobese subjects and subjects with advanced type 2 diabetes mellitus (T2DM). Body weight, fasting serum insulin (FSI), fasting plasma glucose (FPG) and HbA1c were used for nonlinear mixed-effects modelling (using NONMEM v7.2 software). Sources of interstudy variability (ISV) and potential covariates (age, gender, diabetes duration, ethnicity, compliance) were investigated.

RESULTS

An ISV for baseline parameters (body weight and β-cell function) was required. The baseline β-cell function was significantly lower in subjects with advanced T2DM (median difference: Study 2: 15.6%, P < 0.001; Study 3: 22.7%, P < 0.001) than in subjects with newly diagnosed T2DM (Study 1). A reduction in the estimated insulin secretory response in subjects with advanced T2DM was observed but diabetes duration was not a significant covariate.

CONCLUSION

The WHIG model can be used to describe the changes in weight, IS and β-cell function in the diabetic population. IS remained relatively stable between subjects but a large ISV in β-cell function was observed. There was a trend towards decreasing β-cell responsiveness with diabetes duration, and further studies, incorporating subjects with a longer history of diabetes, are required.

Application of the integrated glucose-insulin model for cross-study characterization of T2DM patients on metformin background treatment

AIM

The integrated glucose-insulin (IGI) model is a semi-mechanistic physiological model which can describe the glucose-insulin homeostasis system following various glucose challenge settings. The aim of the present work was to apply the model to a large and diverse population of metformin-only-treated type 2 diabetes mellitus (T2DM) patients and identify patient-specific covariates.

METHODS

Data from four clinical studies were pooled, including glucose and insulin concentration-time profiles from T2DM patients on stable treatment with metformin alone following mixed-meal tolerance tests. The data were collected from a wide range of patients with respect to the duration of diabetes and level of glycaemic control.

RESULTS

The IGI model was expanded by four patient-specific covariates. The level of glycaemic control, represented by baseline glycosylated haemoglobin was identified as a significant covariate for steady-state glucose, insulin-dependent glucose clearance and the magnitude of the incretin effect, while baseline body mass index was a significant covariate for steady-state insulin levels. In addition, glucose dose was found to have an impact on glucose absorption rate. The developed model was used to simulate glucose and insulin profiles in different groups of T2DM patients, across a range of glycaemic control, and it was found accurately to characterize their response to the standard oral glucose challenge.

CONCLUSIONS

The IGI model was successfully applied to characterize differences between T2DM patients across a wide range of glycaemic control. The addition of patient-specific covariates in the IGI model might be valuable for the future development of antidiabetic treatment and for the design and simulation of clinical studies.

Modeling the Disease Progression from Healthy to Overt Diabetes in ZDSD Rats

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 would require a long study duration. A population model using Zucker diabetic Sprague-Dawley (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. Body weight, fasting plasma glucose (FPG), and fasting serum insulin (FSI) were collected biweekly over 24 weeks from ZDSD rats (n = 23) starting 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. 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 glucose-dependent renal effect exerted a two- to sixfold increase on the elimination of glucose. A glucose-dependent weight loss effect towards the end of experiment was implemented. 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 glucotoxicity phenomenon seen in advanced hyperglycemia.