How Consistent is the Relationship between Improved Glucose Control and Modelled Health Outcomes for People with Type 2 Diabetes Mellitus? a Systematic Review

Estimating Risk Factor Time Paths Among People with Type 2 Diabetes and QALY Gains from Risk Factor Management

OBJECTIVES

Most type 2 diabetes simulation models utilise equations mapping out lifetime trajectories of risk factors [e.g. glycated haemoglobin (HbA1c)]. Existing equations, using historic data or assuming constant risk factors, frequently underestimate or overestimate complication rates. Updated risk factor time path equations are needed for simulation models to more accurately predict complication rates.

AIMS

(1) Update United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2) risk factor time path equations; (2) compare quality-adjusted life-years (QALYs) using original and updated equations; and (3) compare QALY gains for reference case simulations using different risk factor equations.

METHODS

Using pooled contemporary data from two randomised trials EXSCEL and TECOS (n = 28,608), we estimated: dynamic panel models of seven continuous risk factors (high-density lipoprotein cholesterol, low density lipoprotein cholesterol, HbA1c, haemoglobin, heart rate, blood pressure and body mass index); two-step models of estimated glomerular filtration rate; and survival analyses of peripheral arterial disease, atrial fibrillation and albuminuria. UKPDS-OM2-derived lifetime QALYs were extrapolated over 70 years using historical and the new risk factor equations.

RESULTS

All new risk factor equation predictions were within 95% confidence intervals of observed values, displaying good agreement between observed and estimated values. Historical risk factor time path equations predicted trial participants would accrue 9.84 QALYs, increasing to 10.98 QALYs using contemporary equations.

DISCUSSION

Incorporating updated risk factor time path equations into diabetes simulation models could give more accurate predictions of long-term health, costs, QALYs and cost-effectiveness estimates, as well as a more precise understanding of the impact of diabetes on patients' health, expenditure and quality of life.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01144338 and NCT00790205.

α-Glucosidase Inhibitory Components from Garcinia pedunculata Fruits

From Garcinia pedunculata Roxb. fruits, two undescribed aromatic compounds including a benzofuran and a depsidone derivative, and a new natural product, together with four known compounds were isolated. Through the analysis of spectroscopic data, high resolution mass spectrum and calculated nuclear magnetic resonance, their structures were determined. The α-glucosidase inhibitory activity of the isolates was evaluated. And compound 3 exhibited a moderate inhibitory effect on α-glucosidase. The molecular docking of compound 3 was performed to elucidate the interaction with α-glucosidase.

Exploring Structural Uncertainty and Impact of Health State Utility Values on Lifetime Outcomes in Diabetes Economic Simulation Models: Findings from the Ninth Mount Hood Diabetes Quality-of-Life Challenge

BACKGROUND

Structural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models.

METHODS

Eleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences.

RESULTS

Substantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (-0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models (P = 0.049).

CONCLUSIONS

Variations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions.HighlightsThe findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs).There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values.

Development of an information system and mobile application for the care of type 2 diabetes patients at the primary care level for the health sector in Mexico: study protocol for a randomized controlled, open-label trial

Background

Providing optimal care for type 2 diabetes (DM2) patients remains a challenge for all healthcare systems. Patients often encounter various barriers in adhering to self-management programs due to lack of knowledge and understanding of self-care activities, lack of individualized and coordinated care, inconvenient and costly education sessions, and poor patient-provider communication. Mobile technologies such as cell phones/smartphones, handheld tablets, and other wireless devices offer new and exciting opportunities for addressing some of these challenges. The purpose of this study is to compare a diabetes management strategy using an information board and a mobile application versus standard care in patients with uncontrolled DM2.

Method

The SANENT (Sistema de Análisis de Enfermedades No Transmisibles) trial is a primary care-based, prospective, two-arm, randomized controlled, open-label, blinded-endpoint study. We aim to recruit 1440 DM2 patients during a period of 6 months until the requested number of participants has been achieved. The total length of the intervention will be 1 year. Both men and women treated for DM2 with an HbA1c > 8.5% and ≥ 20 years of age are eligible to participate in the study. The primary outcome of the study is improved diabetes control measured by changes in HbA1c in the study participants. HbA1c will be measured at baseline, 3-month, 6-month, 9-month, and 12-month follow-up visits in all participants. The main analysis will be based on the intention-to-treat principle. The primary endpoint of the study will be the change in HbA1C within the groups and the differences between the groups. This will be assessed by a repeated measurement approach based on mixed models which contain both fixed effects and random effects.

Discussion

The overall goal of this project is to contribute to the evidence for the use of mobile technology to improve the treatment and regulation of poorly controlled DM2 patients living in Mexico. Our proposed project will show how mobile health technology tools can be used in the treatment of patients with uncontrolled DM2 in primary health care in a Latin American population, and particularly how they could help diabetes patients take better care of themselves.

Trial registration

ClinicalTrials.gov, US National Institutes of Health NCT04974333. Prospectively registered on July 13, 2021. Protocol version number 1, dated August 15th, 2021.

Estimating risk factor progression equations for the UKPDS Outcomes Model 2 (UKPDS 90)

OBJECTIVES

To estimate 13 equations that predict clinically plausible risk factor time paths to inform the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model version 2 (UKPDS-OM2).

METHODS

Data from 5102 UKPDS participants from the 20-year trial, and the 4031 survivors with 10 years further post-trial follow-up, were used to derive equations for the time paths of 13 clinical risk factors: HbA_1c , systolic blood pressure, LDL-cholesterol, HDL-cholesterol, BMI, micro- or macro-albuminuria, creatinine, heart rate, white blood cell count, haemoglobin, estimated glomerular filter rate, atrial fibrillation and peripheral vascular disease (PVD). The incidence of events and death predicted by the UKPDS-OM2 when informed by the new risk factor equations was compared with the observed cumulative rates up to 25 years.

RESULTS

The new equations were based on 24 years of follow-up and up to 65,252 person-years of data. Women were associated with higher values of all continuous risk factors except for haemoglobin. Older age and higher BMI at diagnosis were associated with higher rates of PVD (HR 1.06 and 1.02), atrial fibrillation (HR 1.10 and 1.08) and micro- or macro-albuminuria (HR 1.01 and 1.18). Smoking was associated with higher rates of developing PVD (HR 2.38) and micro- and macro-albuminuria (HR 1.39). The UKPDS-OM2, informed by the new risk factor equations, predicted event rates for complications and death consistent with those observed.

CONCLUSIONS

The new equations allow risk factor time paths beyond observed data, which should improve modelling of long-term health outcomes for people with type 2 diabetes when using the UKPDS-OM2 or other models.

Transparency in Decision Modelling: What, Why, Who and How?

Transparency in decision modelling is an evolving concept. Recently, discussion has moved from reporting standards to open-source implementation of decision analytic models. However, in the debate about the supposed advantages and disadvantages of greater transparency, there is a lack of definition. The purpose of this article is not to present a case for or against transparency, but rather to provide a more nuanced understanding of what transparency means in the context of decision modelling and how it could be addressed. To this end, we review and summarise the discourse to date, drawing on our collective experience. We outline a taxonomy of the different manifestations of transparency, including reporting standards, reference models, collaboration, model registration, peer review and open-source modelling. Further, we map out the role and incentives for the various stakeholders, including industry, research organisations, publishers and decision makers. We outline the anticipated advantages and disadvantages of greater transparency with respect to each manifestation, as well as the perceived barriers and facilitators to greater transparency. These are considered with respect to the different stakeholders and with reference to issues including intellectual property, legality, standards, quality assurance, code integrity, health technology assessment processes, incentives, funding, software, access and deployment options, data protection and stakeholder engagement. For each manifestation of transparency, we discuss the 'what', 'why', 'who' and 'how'. Specifically, their meaning, why the community might (or might not) wish to embrace them, whose engagement as stakeholders is required and how relevant objectives might be realised. We identify current initiatives aimed to improve transparency to exemplify efforts in current practice and for the future.

Effects of Agriophyllum squarrosum extracts on glucose metabolism in KKAy mice and the associated underlying mechanisms

ETHNOPHARMACOLOGICAL RELEVANCE

Agriophyllum squarrosum (L.) Moq. is a traditional Mongol medicine commonly used in the treatment of diabetes.

AIM OF THE STUDY

To examine the effects of Agriophyllum squarrosum extract (ASE) on glucose metabolism in type 2 diabetic KKAy mice, and to investigate the mechanisms underlying these effects.

MATERIAL AND METHODS

KKAy mice were divided into a model control group (MCG), a low-dose Agriophyllum squarrosum extract group (LASEG), a medium-dose Agriophyllum squarrosum extract group (MASEG), a high-dose Agriophyllum squarrosum extract group (HASEG), and a metformin group (MEG). Syngeneic C57BL/6 mice were used as a normal control group (NCG). Drugs were administered to all mice by gavage for 8 weeks. Random blood glucose levels were measured in the mice at baseline and after 2, 4, and 8 weeks of treatment. Glucose tolerance was measured after 6 weeks of drug administration. After 8 weeks, glycated serum proteins (GSP) and advanced glycation end-products (AGEs) in the serum of all mice were measured. Sections of mouse liver tissues were used for periodic acid-Schiff staining (PAS) and the content of hepatic glycogen was determined. Immunohistochemistry was used to determine the effects of ASE on liver phospho-insulin receptor substrate 2 (P-IRS2) protein expression. Western blotting was used to quantify the protein expression levels of phosphatidylinositol 3-kinase (PI3K), AKT, phospho-AKT (S473) (P-AKT), glycogen synthase kinase 3β (GSK3β), and glucose transporters 4 (GLUT4), while PCR was used to quantify the mRNA expression levels of insulin receptor substrate 2 (IRS2), PI3K, AKT, GSK3β, and GLUT4.

RESULTS

ASE treatment decreased random blood glucose levels in type 2 diabetic KKAy mice; increased glucose tolerance; decreased serum GSP and AGEs content; increased glycogen synthesis in liver tissues; upregulated the protein expression levels of PI3K, AKT, GLUT4, and P-IRS2; downregulated the protein expression level of GSK3β in liver tissues; upregulated the mRNA expression levels of IRS2, PI3K, AKT, and GLUT4; and downregulated the mRNA expression level of GSK3β in liver tissues.

CONCLUSION

ASE treatment may increase glucose metabolism in KKAy mice and improve glucose tolerance. The underlying mechanisms of the beneficial effects of ASE may be associated with the increase of glycogen synthesis, the inhibition of AGEs production, the upregulation of IRS2, PI3K, AKT, and GLUT4 protein and mRNA expression, and the downregulation of GSK3β protein and mRNA expression.

Cost-effectiveness of Simple Insulin Infusion Devices Compared to Multiple Daily Injections in Uncontrolled Type 2 Diabetics in the United States Based on a Simulation Model

BACKGROUND

As type 2 diabetes (T2D) progresses, administering basal and bolus insulin through multiple daily injections (MDI) is often required to achieve target control, although many people fail to achieve target levels. Continuous subcutaneous insulin infusion (CSII) treatment with traditional pumps has proven effective in this population, but use remains limited in T2D due to CSII cost and complexity. A new class of simple insulin infusion devices have been developed which are simpler to use and less expensive. This paper assesses at what price one such simple insulin infusion device, PAQ® (Cequr SA, Switzerland), may be cost-effective compared to MDI in people with T2D not in glycemic control in the United States.

METHODS

Published equations were used in a simulation model to project long-term cost-effectiveness over 40 years, combined with data from the recent OpT2mise study, assuming similar efficacy of CSII and simple insulin infusion. Cost-effectiveness was pre-defined in relation to per capita gross domestic product (GDP), where incremental cost-effectiveness ratios below 1X the per capita GDP per quality-adjusted life year (QALY) gained were defined as "highly cost-effective" and below 3X GDP per capita as "cost-effective."

RESULTS

Simple insulin infusion resulted in 0.17 QALYs gained per patient compared to MDI, along with lifetime cost-savings of USD 66 883 per person due to reduced insulin use and less complications. Analyses on price sensitivity of simple insulin infusion indicated that a device such as the PAQ is cost-effective compared with MDI up to price points of around USD 17 per day.

CONCLUSIONS

For people with T2D not in glycemic control on MDI, simple insulin infusion devices such as PAQ have the potential to be highly cost-effective in the United States.