Decision models of prediabetes populations: A systematic review

Comparative Analysis of Biomarkers in Type 2 Diabetes Patients With and Without Comorbidities: Insights Into the Role of Hypertension and Cardiovascular Disease

Background

Type 2 diabetes mellitus (T2DM) are 90% of diabetes cases, and its prevalence and incidence, including comorbidities, are rising worldwide. Clinically, diabetes and associated comorbidities are identified by biochemical and physical characteristics including glycemia, glycated hemoglobin (HbA1c), and tests for cardiovascular, eye and kidney disease.

Objectives

Diabetes may have a common etiology based on inflammation and oxidative stress that may provide additional information about disease progression and treatment options. Thus, identifying high-risk individuals can delay or prevent diabetes and its complications.

Design

In patients with or without hypertension and cardiovascular disease, as part of progression from no diabetes to T2DM, this research studied the changes in biomarkers between control and prediabetes, prediabetes to T2DM, and control to T2DM, and classified patients based on first-attendance data. Control patients and patients with hypertension, cardiovascular, and with both hypertension and cardiovascular diseases are 156, 148, 61, and 216, respectively.

Methods

Linear discriminant analysis is used for classification method and feature importance, This study examined the relationship between Humanin and mitochondrial protein (MOTSc), mitochondrial peptides associated with oxidative stress, diabetes progression, and associated complications.

Results

MOTSc, reduced glutathione and glutathione disulfide ratio (GSH/GSSG), interleukin-1β (IL-1β), and 8-isoprostane were significant (P < .05) for the transition from prediabetes to t2dm, highlighting importance of mitochondrial involvement. complement component 5a (c5a) is a biomarker associated with disease progression and comorbidities, gsh gssg, monocyte chemoattractant protein-1 (mcp-1), 8-isoprostane being most important biomarkers.

Conclusions

Comorbidities affect the hypothesized biomarkers as diabetes progresses. Mitochondrial oxidative stress indicators, coagulation, and inflammatory markers help assess diabetes disease development and provide appropriate medications. Future studies will examine longitudinal biomarker evolution.

Cost saving analysis of prediabetes intervention modalities in comparison with inaction using Markov state transition model-A multiregional case study

BACKGROUND

Prediabetes management is a priority for policymakers globally, to avoid/delay type 2 diabetes (T2D) and reduce severe, costly health consequences. Countries moving from low to middle income are most at risk from the T2D "epidemic" and may find implementing preventative measures challenging; yet prevention has largely been evaluated in developed countries.

METHODS

Markov cohort simulations explored costs and benefits of various prediabetes management approaches, expressed as "savings" to the public health care system, for three countries with high prediabetes prevalence and contrasting economic status (Poland, Saudi Arabia, Vietnam). Two scenarios were compared up to 15 y: "inaction" (no prediabetes intervention) and "intervention" with metformin extended release (ER), intensive lifestyle change (ILC), ILC with metformin (ER), or ILC with metformin (ER) "titration."

RESULTS

T2D was the highest-cost health state at all time horizons due to resource use, and inaction produced the highest T2D costs, ranging from 9% to 34% of total health care resource costs. All interventions reduced T2D versus inaction, the most effective being ILC + metformin (ER) "titration" (39% reduction at 5 y). Metformin (ER) was the only strategy that produced net saving across the time horizon; however, relative total health care system costs of other interventions vs inaction declined over time up to 15 y. Viet Nam was most sensitive to cost and parameter changes via a one-way sensitivity analysis.

CONCLUSIONS

Metformin (ER) and lifestyle interventions for prediabetes offer promise for reducing T2D incidence. Metformin (ER) could reduce T2D patient numbers and health care costs, given concerns regarding adherence in the context of funding/reimbursement challenges for lifestyle interventions.

Evaluating the Long-Term Cost-Effectiveness of the English NHS Diabetes Prevention Programme using a Markov Model

BACKGROUND

In 2016, England launched the largest nationwide diabetes mellitus prevention programme, the NHS Diabetes Prevention Programme (NHS DPP). This paper seeks to evaluate the long-term cost-effectiveness of this programme.

METHODS

A Markov cohort state transition model was developed with a 35-year time horizon and yearly cycles to compare referral to the NHS DPP to usual care for individuals with non-diabetic hyperglycaemia. The modelled cohort of individuals mirrored the age profile of referrals received by the programme by April 2020. A health system perspective was taken, with costs in UK £ Sterling (price year 2020) and outcomes in terms of quality-adjusted life-years (QALYs). Probabilistic analysis with 10,000 Monte Carlo simulations was used. Several sensitivity analyses were conducted to explore the uncertainty surrounding the base case results, particularly varying the length of time for which the effectiveness of the programme was expected to last.

RESULTS

In the base case, using only the observed effectiveness of the NHS DPP at 3 years, it was found that the programme is likely to dominate usual care, by generating on average 40.8 incremental QALYs whilst saving £135,755 in costs for a cohort of 1000. At a willingness to pay of £20,000 per QALY, 98.1% of simulations were on or under the willingness-to-pay threshold. Scaling this up to the number of referrals actually received by the NHS DPP prior to April 2020, cost savings of £71.4 million were estimated over the 35-year time horizon and an additional 21,472 QALYs generated. These results are robust to several sensitivity analyses.

CONCLUSION

The NHS DPP is likely to be cost-effective. Indeed, in the majority of the simulations, the NHS DPP was cost-saving and generated greater QALYs, dominating usual care. This research should serve as evidence to support the continued investment or recommissioning of diabetes prevention programmes.

Model and Empirical Data-Based Cost-Utility Studies of Continuous Glucose Monitoring in Individuals with Type 1 Diabetes: A Protocol of a Systematic Review on Methodology and Quality

INTRODUCTION

This review aims to critically appraise differences in methodology and quality of model-based and empirical-data-based cost-utility studies to address key limitations, opportunities, and challenges to inform future cost-utility analyses of continuous glucose monitoring (CGM) in type 1 diabetes. This protocol is registered at PROSPERO (CRD42023391284).

METHODS

The review will be conducted in accordance with the PRISMA guideline for systematic reviews. Searches will be conducted in MEDLINE, Embase, Web of Science, Cochrane Library, and Econlit from 2000 to January 2023. Model and empirical data-based studies evaluating the cost-utility of any CGM system in type 1 diabetes will be considered for inclusion. Studies that only report on cost per life year or any other clinical outcome, or reporting only costs or only clinical outcomes studies in type 2 diabetes populations, and studies on bi-hormonal closed loops and do-it-yourself hybrid closed loop devices will be excluded. Two reviewers will independently screen each study for inclusion. Data on the intervention, population, model settings (such as perspective, time horizon), model type and structure, clinical outcomes used to populate the model, validation, and uncertainty will be extracted and qualitatively synthesised. Quality will be assessed using the Philips et al. 2006 (model-based studies) or Consensus Health Economic Criteria (empirical data-based studies) checklists. Model validation will be assessed using the AdViSHE checklist.

DISCUSSION

Now that CGM is being used more broadly in practice, critical appraisal of existing cost-utility methodology and quality is important to inform future cost-utility analyses of CGM in type 1 diabetes in various settings.

Nonlinear relationship between atherogenic index of plasma and the risk of prediabetes: a retrospective study based on Chinese adults

BACKGROUND

The atherogenic index of plasma (AIP) can reflect the burden of atherosclerosis. Hyperglycemia is one of the leading causes of atherosclerosis. However, the relationship between AIP and prediabetes is rarely studied. Therefore, we aimed to explore the relationship between AIP and prediabetes.

METHODS

This retrospective cohort study recruited 100,069 Chinese adults at the Rich Healthcare Group from 2010 to 2016. AIP was calculated according to Log10 (triglyceride/high-density lipoprotein cholesterol) formula. Cox regression method, sensitivity analyses and subgroup analyses were used to examine the relationship between AIP and prediabetes. Cox proportional hazards regression with cubic spline functions and smooth curve fitting was performed to explore the non-linearity between AIP and prediabetes. The two-piece Cox proportional hazards regression model was used to determine the inflection point of AIP on the risk of prediabetes.

RESULTS

After adjusting for confounding covariates, AIP was positively associated with prediabetes (HR: 1.41, 95%CI: 1.31-1.52, P < 0.0001). The two-piecewise Cox proportional hazards regression model discovered that the AIP's inflection point was 0.03 (P for log-likelihood ratio test < 0.001). AIP was positively associated with the risk of prediabetes when AIP ≤ 0.03 (HR: 1.90, 95%CI: 1.66-2.16, P < 0.0001). In contrast, When AIP > 0.03, their association was not significant (HR: 1.04, 95%CI: 0.91-1.19, P = 0.5528).

CONCLUSION

This study shows that AIP was positively and non-linearly associated with the risk of prediabetes after adjusting for other confounding factors. When AIP ≤ 0.03, AIP was positively associated with the risk of prediabetes.

Health-related quality of life for normal glycaemia, prediabetes and type 2 diabetes mellitus: Cross-sectional analysis of the ADDITION-PRO study

AIMS

We estimated and compared health-related quality of life for individuals with normal glucose tolerance, prediabetes and diabetes.

METHODS

Participants in the ADDITION-PRO study, Denmark, who attended a health assessment between 2009 and 2011, and who completed the 3-level EuroQoL 5-dimensions (EQ-5D-3L) questionnaire were included. For the present study, they were classified as normal glucose tolerance, prediabetes and diabetes (screen-detected and known) using the 2019 American Diabetes Association criteria. Prediabetes was defined as impaired fasting glucose, impaired glucose tolerance or HbA1c between 5.7-6.4% (39-47 mmol/mol). EQ-5D-3L data were converted into utility scores using Danish and UK values, where '1' equals full health and '0' equals death. Regression models estimated the association between utility and the different glucose health states.

RESULTS

The mean EQ-5D-3L score in the sample population was 0.86 ± 0.17 (median 0.85, interquartile range 0.76 to 1) using UK values. Almost half of the sample (48%) reported full health with an EQ-5D score of '1'. Individuals with known diabetes reported the lowest EQ-5D-3L utility scores (0.81 ± 0.20), followed by individuals with screen-detected diabetes (0.85 ± 0.19), prediabetes (0.86 ± 0.17) and normal glucose tolerance (0.90 ± 0.15). The differences were statistically significant for normal glucose and known diabetes relative to prediabetes, after adjusting for sex, age, smoking, BMI and physical activity. These findings also held using Danish values albeit the differences were of smaller magnitude.

CONCLUSIONS

Having prediabetes and diabetes was significantly associated with lower health-related quality of life relative to normal glucose tolerance. Our estimates will be useful to inform the value of interventions to prevent diabetes or prediabetes.

The Variation and Correlation of Serum Adiponectin, Nesfatin-1, IL-6, and TNF-α Levels in Prediabetes

BACKGROUND

The variation and correlation among adiponectin, nesfatin-1, tumor necrosis factor α (TNF-α), and interleukin 6 (IL-6), which may be involved in the development of the decline of health into prediabetes and diabetes, have not been elucidated. This study aims to investigate the roles of these cytokines in this process.

METHODS

Seventy-two type 2 diabetes mellitus (T2DM) patients, 75 prediabetics, and 72 healthy individuals were enrolled in our case control study. Serum adiponectin, nesfatin-1, TNF-α, and IL-6 were tested with appropriate kits, and primary data were analyzed with correct methods.

RESULTS

Serum levels of each cytokine in patients with prediabetes were between T2DM and the healthy, and significant differences were found among them. TNF-α and nesfatin-1 levels in T2DM were obviously different compared to prediabetes or the healthy; IL-6 and adiponectin levels in the healthy group were significantly changed in contrast to prediabetes or T2DM. Correlation analysis found that in prediabetics, adiponectin was positively correlated with TNF-α (R = 0.2939, P = 0.0105) and IL-6 (R = 0.3918, P = 0.0005), and their relationship was greatly strengthened in prediabetes accompanied by insulin resistance (TNF-α: R = 0.7732, P < 0.0001, IL-6: R = 0.6663, P = 0.0005). We also demonstrated that declined adiponectin (OR = 6.238, P = 0.019) and nesfatin-1 (OR = 2.812, P = 0.01) and elevated TNF-α (OR = 5.541, P = 0.001) were risk factors for prediabetes toward diabetes.

CONCLUSIONS

This research proved significant variations of adiponectin, nesfatin-1, IL-6, and TNF-α levels in the healthy, prediabetics, and T2DM, suggesting a slow and gradual change during the progression from a healthy condition toward diabetes via prediabetes.

Decision models in type 2 diabetes mellitus: A systematic review

AIMS

To reduce the burden of type 2 diabetes (T2DM), the disease decision model plays a vital role in supporting decision-making. Currently, there is no comprehensive summary and assessment of the existing decision models for T2DM. The objective of this review is to provide an overview of the characteristics and capabilities of published decision models for T2DM. We also discuss which models are suitable for different study demands.

MATERIALS AND METHODS

Four databases (PubMed, Web of Science, Embase, and the Cochrane Library) were electronically searched for papers published from inception to August 2020. Search terms were: "Diabetes-Mellitus, Type 2", "cost-utility", "quality-of-life", and "decision model". Reference lists of the included studies were manually searched. Two reviewers independently screened the titles and abstracts following the inclusion and exclusion criteria. If there was insufficient information to include or exclude a study, then a full-text version was sought. The extracted information included basic information, study details, population characteristics, basic modeling methodologies, model structure, and data inputs for the included applications, model outcomes, model validation, and uncertainty.

RESULTS

Fourteen unique decision models for T2DM were identified. Markov chains and risk equations were utilized by four and three models, respectively. Three models utilized both. Except for the Archimedes model, all other models (n = 13) implemented an annual cycle length. The time horizon of most models was flexible. Fourteen models had differences in the division of health states. Ten models emphasized macrovascular and microvascular complications. Six models included adverse events. Majority of the models (n = 11) were patient-level simulation models. Eleven models simulated annual changes in risk factors (body mass index, glycemia, HbA1c, blood pressure (systolic and/or diastolic), and lipids (total cholesterol and/or high-density lipoprotein)). All models reported the main data sources used to develop health states of complications. Most models (n = 11) could deal with the uncertainty of models, which were described in varying levels of detail in the primary studies. Eleven studies reported that one or more validation checks were performed.

CONCLUSIONS

The existing decision models for T2DM are heterogeneous in terms of the level of detail in the classification of health states. Thus, more attention should be focused on balancing the desired level of complexity against the required level of transparency in the development of T2DM decision models.

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.

Optimizing diabetes screening frequencies for at-risk groups

There is strong evidence that diabetes is underdiagnosed in the US: the Centers for Disease Control and Prevention (CDC) estimates that approximately 25% of diabetic patients are unaware of their condition. To encourage timely diagnosis of at-risk patients, we develop screening guidelines stratified by body mass index (BMI), age, and prior test history by using a Partially Observed Markov Decision Process (POMDP) framework to provide more personalized screening frequency recommendations. We identify structural results that prove the existence of threshold solutions in our problem and allow us to determine the relative timing and frequency of screening given different risk profiles. We then use nationally representative empirical data to identify a policy that provides the optimal action (screen or wait) every six months from age 45 to 90. We find that the current screening guidelines are suboptimal, and the recommended diabetes screening policy should be stratified by age and by finer BMI thresholds than in the status quo. We identify age ranges and BMI categories for which relatively less or more screening is needed compared to the existing guidelines to help physicians target patients most at risk. Compared to the status quo, we estimate that an optimal screening policy would generate higher net monetary benefits by $3,200-$3,570 and save $120-$1,290 in health expenditures per individual in the US above age 45.

An Innovative Approach for Decision-Making on Designing Lifestyle Programs to Reduce Type 2 Diabetes on Dutch Population Level Using Dynamic Simulations

The number of individuals suffering from type 2 diabetes is dramatically increasing worldwide, resulting in an increasing burden on society and rising healthcare costs. With increasing evidence supporting lifestyle intervention programs to reduce type 2 diabetes, and the use of scenario simulations for policy support, there is an opportunity to improve population interventions based upon cost–benefit analysis of especially complex lifestyle intervention programs through dynamic simulations. In this article, we used the System Dynamics (SD) modeling methodology aiming to develop a simulation model for policy makers and health professionals to gain a clear understanding of the patient journey of type 2 diabetes mellitus and to assess the impact of lifestyle intervention programs on total cost for society associated with prevention and lifestyle treatment of pre-diabetes and type 2 diabetes in The Netherlands. System dynamics describes underlying structure in the form of causal relationships, stocks, flows, and delays to explore behavior and simulate scenarios, in order to prescribe intervention programs. The methodology has the opportunity to estimate and simulate the consequences of unforeseen interactions in order to prescribe intervention programs based on scenarios tested through “what-if” experiments. First, the extensive knowledge of diabetes, current available data on the type 2 diabetes population, lifestyle intervention programs, and associated cost in The Netherlands were captured in one simulation model. Next, the relationships between leverage points on the growth of type 2 diabetes population were based upon available data. Subsequently, the cost and benefits of future lifestyle intervention programs on reducing diabetes were simulated, identifying the need for an integrated adaptive design of lifestyle programs while collecting the appropriate data over time. The strengths and limitations of scenario simulations of complex lifestyle intervention programs to improve the (cost)effectiveness of these programs to reduce diabetes in a more sustainable way compared to usual care are discussed.

Risk Factors and Wellness Measures Associated with Prediabetes and Newly Diagnosed Type 2 Diabetes Mellitus in Hispanic Adults

Background: To characterize the associations of clinical risk factors, lifestyle factors, and wellness measures with prediabetes and new type 2 diabetes mellitus (T2DM) diagnosis in Hispanic adults and guide primary prevention. Methods: Sangre Por Salud Biobank enrolled 3733 Hispanic adults from Phoenix, AZ, United States, from 2013 to 2018. This analysis included participants with euglycemia, prediabetes, or new T2DM diagnosis (i.e., no prior T2DM diagnosis) at enrollment. Participants completed a baseline questionnaire on cardiometabolic risk factors and wellness measures and provided biometric measurements. The associations of factors and measures with odds (95% confidence interval) of prediabetes and new T2DM diagnosis were analyzed in logistic regression models. Results: Among 3299 participants with euglycemia (n = 1301), prediabetes (n = 1718), and new T2DM diagnosis (n = 280) at enrollment, 72% were women (n = 2376/3299). In adjusted models, most cardiometabolic risk factors were positively associated with prediabetes and new T2DM diagnosis, with stronger associations for new T2DM diagnosis. Obesity (body mass index ≥30 kg/m² vs. lower) was associated with higher odds of new T2DM diagnosis (3.14 [2.30-4.28]; P < 0.01) than prediabetes versus euglycemia (1.96 [1.66-2.32]; P < 0.01) and Interaction (P = 0.01). Similarly, waist circumference, family history of diabetes, and average systolic and diastolic blood pressure were associated with higher odds of new T2DM diagnosis versus euglycemia than prediabetes versus euglycemia. Using stepwise logistic regression modeling, a parsimonious model of age, family history of diabetes, waist circumference, diastolic blood pressure, passive tobacco exposure, and self-rated general health were associated with new T2DM diagnosis versus euglycemia. Conclusions: In Hispanic adults, modifiable cardiometabolic and lifestyle factors were associated with prediabetes and new T2DM diagnosis. Personalized interventions targeting these factors and measures could guide T2DM primary prevention efforts among Hispanic adults.

Benchmarking the Cost-Effectiveness of Interventions Delaying Diabetes: A Simulation Study Based on NAVIGATOR Data

OBJECTIVE

To estimate using the UK Prospective Diabetes Study Outcomes Model Version 2 (UKPDS-OM2) the impact of delaying type 2 diabetes onset on costs and quality-adjusted life expectancy using trial participants who developed diabetes in the NAVIGATOR (Nateglinide And Valsartan in Impaired Glucose Tolerance Outcomes Research) study.

RESEARCH DESIGN AND METHODS

We simulated the impact of delaying diabetes onset by 1-9 years, utilizing data from the 3,058 of 9,306 NAVIGATOR trial participants who developed type 2 diabetes. Costs and utility weights associated with diabetes and diabetes-related complications were obtained for the U.S. and U.K. settings, with costs expressed in 2017 values. We estimated discounted lifetime costs and quality-adjusted life years (QALYs) with 95% CIs.

RESULTS

Gains in QALYs increased from 0.02 (U.S. setting, 95% CI 0.01, 0.03) to 0.15 (U.S. setting, 95% CI 0.10, 0.21) as the imposed time to diabetes onset was increased from 1 to 9 years, respectively. Savings in complication costs increased from $1,388 (95% CI $1,092, $1,669) for a 1-year delay to $8,437 (95% CI $6,611, $10,197) for a delay of 9 years. Interventions costing up to $567-$2,680 and £201-£947 per year would be cost-effective at $100,000 per QALY and £20,000 per QALY thresholds in the U.S. and U.K., respectively, as the modeled delay in diabetes onset was increased from 1 to 9 years.

CONCLUSIONS

Simulating a hypothetical diabetes-delaying intervention provides guidance concerning the maximum cost and minimum delay in diabetes onset needed to be cost-effective. These results can inform the ongoing debate about diabetes prevention strategies and the design of future intervention studies.

Developing and testing a discrete event simulation model to evaluate budget impacts of diabetes prevention programs

Type 2 diabetes (T2D) is one of the most rapidly increasing non-communicable diseases worldwide. Lifestyle interventions are effective in preventing T2D but also resource intensive. This study evaluated with discrete event simulation (DES) the relative budget impacts of three hypothetical diabetes prevention programs (DPP), including group-based contact intervention, digital program with human coaching and fully automated program. The data for simulation were derived from research literature and national health and population statistics. The model was constructed using the iGrafx Process for Six Sigma software and simulations were carried out for 10 years. All simulated interventions produced cost savings compared to the situation without any intervention. However, this was a modeling study and future studies are needed to verify the results in real-life. Decision makers could benefit the predictive models regarding the long-term effects of diabetes prevention interventions, but more data is needed in particular on the usage, acceptability, effectiveness and costs of digital intervention tools.

The Impact of Including Costs and Outcomes of Dementia in a Health Economic Model to Evaluate Lifestyle Interventions to Prevent Diabetes and Cardiovascular Disease

Objectives

Economic evaluations of lifestyle interventions, which aim to prevent diabetes/cardiovascular disease (CVD), have not included dementia. Lifestyle interventions decrease dementia risk and extend life expectancy, leading to competing effects on health care costs. We aim to demonstrate the feasibility of including dementia in a public health cost-effectiveness analysis and quantify the overall impacts accounting for these competing effects.

Methods

The School for Public Health Research (SPHR) diabetes prevention model describes individuals’ risk of type 2 diabetes, microvascular outcomes, CVD, congestive heart failure, cancer, osteoarthritis, depression, and mortality in England. In version 3.1, we adapted the model to include dementia using published data from primary care databases, health surveys, and trials of dementia to describe dementia incidence, diagnosis, and disease progression. We estimate the impact of dementia on lifetime costs and quality-adjusted life years (QALYs) gained of the National Health Service diabetes prevention program (NHS DPP) from an NHS/personal social services perspective with 3 scenarios: 1) no dementia, 2) dementia only, and 3) reduced dementia risk. Subgroup, parameter, and probabilistic sensitivity analyses were conducted.

Results

The lifetime cost savings of the NHS DPP per patient were £145 in the no-dementia scenario, £121 in the dementia-only scenario, and £167 in the reduced dementia risk scenario. The QALY gains increased by 0.0006 in dementia only and 0.0134 in reduced dementia risk. Dementia did not alter the recommendation that the NHS/DPP is cost-effective.

Conclusions

Including dementia into a model of lifestyle interventions was feasible but did not change policy recommendations or modify health economic outcomes. The impact on health economic outcomes was largest where a direct impact on dementia incidence was assumed, particularly in elderly populations.

Increased risks of different grades of non-alcoholic fatty liver disease in prediabetic subjects with impaired fasting glucose and glucose tolerance, including the isolated glycosylated hemoglobin levels of 5.7-6.4% in a Chinese population

AIMS/INTRODUCTION

Contrary to the results of the majority of studies on diabetes, there are some conflicting results regarding the relationship between non-alcoholic fatty liver disease (NAFLD) and prediabetes. No study has investigated the relationship between isolated glycated hemoglobin (HbA1c) in the range of 5.7-6.4% (HbA1c 5.7-6.4%) and NAFLD. Our aim was to investigate the effect of different glycemic statuses on NAFLD concomitantly categorized by fasting plasma glucose, 2-h plasma glucose and HbA1c levels.

MATERIALS AND METHODS

NAFLD was classified into three groups by ultrasonographic examination results: normal, mild and moderate-to-severe. Glycemic status was divided into five groups: normoglycemia, isolated HbA1c 5.7-6.4%, impaired fasting glucose without impaired glucose tolerance (IGT), IGT and newly diagnosed diabetes. For multivariable logistic regression analyses, the outcome variable was the classified three grades of fatty changes in the liver after adjusting for other potential risk covariables.

RESULTS

In this cross-sectional research, a total of 8,571 eligible individuals were enrolled and divided into three groups: 5,499 without fatty liver, 2,113 with mild NAFLD and 959 with moderate-to-severe NAFLD. Multivariable logistic regression analysis showed that IGT, impaired fasting glucose without IGT and isolated HbA1c 5.7-6.4% were associated with a higher risk of NAFLD in addition to newly diagnosed diabetes. Other positively predictive variables were male sex, obesity, overweight, central obesity, increased triglyceride and C-reactive protein >1 mg/L. Negatively associated factors were elevated high-density lipoprotein cholesterol levels.

CONCLUSIONS

Besides diabetes, the increased risks of different grades of NAFLD were found for prediabetic individuals categorized by impaired fasting glucose without IGT, IGT and isolated HbA1c 5.7-6.4%.

How valid are projections of the future prevalence of diabetes? Rapid reviews of prevalence-based and Markov chain models and comparisons of different models' projections for England

OBJECTIVES

To examine validity of prevalence-based models giving projections of prevalence of diabetes in adults, in England and the UK, and of Markov chain models giving estimates of economic impacts of interventions to prevent type 2 diabetes (T2D).

METHODS

Rapid reviews of both types of models. Estimation of the future prevalence of T2D in England by Markov chain models; and from the trend in the prevalence of diabetes, as reported in the Quality and Outcomes Framework (QOF), estimated by ordinary least squares regression analysis.

SETTING

Adult population in England and UK.

MAIN OUTCOME MEASURE

Prevalence of T2D in England and UK in 2025.

RESULTS

The prevalence-based models reviewed use sample estimates of past prevalence rates by age and sex and projected population changes. Three most recent models, including that of Public Health England (PHE), neither take account of increases in obesity, nor report Confidence Intervals (CIs). The Markov chain models reviewed use transition probabilities between states of risk and death, estimated from various sources. None of their accounts give the full matrix of transition probabilities, and only a minority report tests of validation. Their primary focus is on estimating the ratio of costs to benefits of preventive interventions in those with hyperglycaemia, only one reported estimates of those developing T2D in the absence of a preventive intervention in the general population.Projections of the prevalence of T2D in England in 2025 were (in millions) by PHE, 3.95; from the QOF trend, 4.91 and by two Markov chain models, based on our review, 5.64 and 9.07.

CONCLUSIONS

To inform national policies on preventing T2D, governments need validated models, designed to use available data, which estimate the scale of incidence of T2D and survival in the general population, with and without preventive interventions.

Pericoronary fat inflammation and Major Adverse Cardiac Events (MACE) in prediabetic patients with acute myocardial infarction: effects of metformin

Background/objectives

Pericoronary adipose tissue inflammation might lead to the development and destabilization of coronary plaques in prediabetic patients. Here, we evaluated inflammation and leptin to adiponectin ratio in pericoronary fat from patients subjected to coronary artery bypass grafting (CABG) for acute myocardial infarction (AMI). Furthermore, we compared the 12-month prognosis of prediabetic patients compared to normoglycemic patients (NG). Finally, the effect of metformin therapy on pericoronary fat inflammation and 12-months prognosis in AMI-prediabetic patients was also evaluated.

Methods

An observational prospective study was conducted on patients with first AMI referred for CABG. Participants were divided in prediabetic and NG-patients. Prediabetic patients were divided in two groups; never-metformin-users and current-metformin-users receiving metformin therapy for almost 6 months before CABG. During the by-pass procedure on epicardial coronary portion, the pericoronary fat was removed from the surrounding stenosis area. The primary endpoints were the assessments of Major-Adverse-Cardiac-Events (MACE) at 12-month follow-up. Moreover, inflammatory tone was evaluated by measuring pericoronary fat levels of tumor necrosis factor-α (TNF-α), sirtuin 6 (SIRT6), and leptin to adiponectin ratio. Finally, inflammatory tone was correlated to the MACE during the 12-months follow-up.

Results

The MACE was 9.1% in all prediabetic patients and 3% in NG-patients. In prediabetic patients, current-metformin-users presented a significantly lower rate of MACE compared to prediabetic patients never-metformin-users. In addition, prediabetic patients showed higher inflammatory tone and leptin to adiponectin ratio in pericoronary fat compared to NG-patients (P < 0.001). Prediabetic never-metformin-users showed higher inflammatory tone and leptin to adiponectin ratio in pericoronary fat compared to current-metformin-users (P < 0.001). Remarkably, inflammatory tone and leptin to adiponectin ratio was significantly related to the MACE during the 12-months follow-up.

Conclusion

Prediabetes increase inflammatory burden in pericoronary adipose tissue. Metformin by reducing inflammatory tone and leptin to adiponectin ratio in pericoronary fat may improve prognosis in prediabetic patients with AMI.

Trial registration Clinical Trial NCT03360981, Retrospectively Registered 7 January 2018

Decision models of prediabetes populations: A systematic review

AIMS

With evidence supporting the use of preventive interventions for prediabetes populations and the use of novel biomarkers to stratify the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. Our aim is to summarize and assess the quality and validity of decision models and model-based economic evaluations of populations with prediabetes, to evaluate their potential use for the assessment of novel prevention strategies and to discuss the knowledge gaps, challenges and opportunities.

MATERIALS AND METHODS

We searched Medline, Embase, EconLit and NHS EED between 2000 and 2018 for studies reporting computer simulation models of the natural history of individuals with prediabetes and/or we used decision models to evaluate the impact of treatment strategies on these populations. Data were extracted following PRISMA guidelines and assessed using modelling checklists. Two reviewers independently assessed 50% of the titles and abstracts to determine whether a full text review was needed. Of these, 10% was assessed by each reviewer to cross-reference the decision to proceed to full review. Using a standardized form and double extraction, each of four reviewers extracted 50% of the identified studies.

RESULTS

A total of 29 published decision models that simulate prediabetes populations were identified. Studies showed large variations in the definition of prediabetes and model structure. The inclusion of complications in prediabetes (n = 8) and type 2 diabetes (n = 17) health states also varied. A minority of studies simulated annual changes in risk factors (glycaemia, HbA1c, blood pressure, BMI, lipids) as individuals progressed in the models (n = 7) and accounted for heterogeneity among individuals with prediabetes (n = 7).

CONCLUSIONS

Current prediabetes decision models have considerable limitations in terms of their quality and validity and do not allow evaluation of stratified strategies using novel biomarkers, highlighting a clear need for more comprehensive prediabetes decision models.