Impact of Changes in HbA^Sub 1c^, Lipids and Blood Pressure on Long- Term Outcomes in Type 2 Diabetes Patients: An Analysis Using the CORE Diabetes Model

Key words: Blood pressure – Complication costs – Diabetes – Total cholesterol – High-density lipoprotein – Glycaemic control – Life expectancy -Modelling

SUMMARY

Objectives: Various factors influence the risk of complications in type 2 diabetes patients. The isolated impact of single risk factors on long-term outcomes is unclear. The aim of this study was to calculate the projected effects on life expectancy (LE), quality- adjusted LE (QALE) and total costs of complications (TC) of 10% improvements in baseline levels of either total cholesterol (T- CHOL), high-density lipoprotein cholesterol (HDL), systolic blood pressure (SBP), glycosylated haemoglobin (HbA^sub 1c^), and all four parameters combined.

Methods: A cohort of newly diagnosed patients (baseline age 52 years, HbA^sub 1c^ 9.1%, SBP 137 mmHg, T-CHOL 212 mg/dL, and HDL 39 mg/dL) was defined. The CORE Diabetes Model was used to simulate LE, QALE and TC (US third-party payer perspective discounted at 3% annually) over patients’ lifetimes, assuming no change in risk factors, an isolated 10% improvement in each parameter, or a 10% improvement in all parameters simultaneously.

Results: Improved HbA^sub 1c^ led to increases in LE and QALE of 1.00 and 0.81 years respectively, and decreased TC of (US) $10,800/ patient. Improved SBP led to improvements in LE and QALE of 0.67 and 0.55 years respectively and decreased TC of $7,049. Decreased T- CHOL led to improvements in LE and QALE of 0.29 and 0.20 years, respectively, and increased TC of $1,923. Increased HDL led to improvements in LE and QALE of 0.28 and 0.18 years respectively, and increased TC of $2,162. Simultaneous improvements in all parameters led to projected improvements in LE and QALE of 2.17 and 1.72 years respectively, and decreased TC of $14,533.

Conclusions: Combined improvements in HbA^sub 1c^, lipid levels and SBP produced the greatest benefits in terms of LE, QALE and TC. A 10% improvement in HbA^sub 1c^ had the greatest impact on these three outcomes.

Introduction

Type 2 diabetes is a major health problem in the industrialised world and its incidence is increasing worldwide1-4. The majority of the costs associated with diabetes can be attributed to its complications, including micro- and macrovascular disease, hypertension and atherogenic serum lipid profiles5-7. Traditionally, treatment of subjects with type 2 diabetes focused on effective glycaemic control to reduce the risk of microvascular complications. However, in recent years treatment designed to reduce the risk of macrovascular complications, such as aggressive blood pressure control and reducing serum cholesterol levels, has been recommended8.

The risks associated with poor glycaemic control and hypertension in patients with type 2 diabetes are well established. The United Kingdom Prospective Diabetes Study (UKPDS) provided data on these risks in a long-term analysis of newly-diagnosed type 2 diabetes subjects9-12. Moreover, Turner et al. published data from the same study showing that dyslipidaemia in individuals with type 2 diabetes increases the risk of coronary heart disease (CHD), including myocardial infarction13. Increased LDL (low-density lipoprotein cholesterol) and decreased HDL (high-density lipoprotein cholesterol) were independent risk factors for the development of CHD within 5 years.

Dyslipidaemia is common in patients with diabetes. The United States Centers for Disease Control and Prevention (CDC) have published data indicating that 97% of patients with diabetes had dyslipidaemia (defined as LDL > 100mg/dL, HDL < 45mg/dL, or triglycerides (TG) > 150 mg/dL)14. This high prevalence of dyslipidaemia is supported by data from Jovanovic et al, which showed that 33% of diabetic patients have LDL levels > 100mg/dL and 25% have HDL < 45mg/dL15. Similarly, in Australia 22.4% of men and 24.1% of women with type 2 diabetes have low HDL levels (< 40 mg/ dL)16. Total cholesterol levels were high in 57.7% of men and 69.2% of women in this population.

To control serum lipid levels and decrease cardiovascular risk, therapeutic lifestyle modification and optimal glycaemic control are usually the first steps for subjects with diabetes (interested readers can find clinical practice recommendations for the treatment of diabetes on the website of the American Diabetes Association17). Therapeutic lifestyle changes commonly consist of increased physical activity and following a healthy diet, which includes decreasing saturated fat intake (known to be the principle dietary determinant of plasma LDL) and reducing dietary cholesterol (diabetes sufferers appear to be more sensitive to dietary cholesterol than the general population)18,19. Dietary fat restriction and weight loss lead to decreased plasma TG and a modest lowering of plasma LDL. Regular physical activity can also reduce plasma TG and improve insulin sensitivity. Pharmacological interventions to improve glycaemic control often lower TG levels. However, glucose-lowering agents generally do not alter, or only have a modest effect on, HDL levels20. Interventions recommended to control serum levels include statins to lower LDL and fibrates to lower TG levels. Nicotinic acid, for example, is usually recommended in combination with a statin as a second-line therapy to control HDL levels in non- diabetic patients, but due to its adverse effects on glycaemic control, first-line use of nicotinic acid should be utilised with caution in diabetes patients21.

Data from the UKPDS and other studies were used by the CDC Diabetes Cost-effectiveness Group to create a model of disease progression and treatment patterns, with which an analysis of the costeffectiveness of intensive glycaemic control, intensified hypertension control and serum cholesterol reduction in type 2 diabetes was performed22. The study simulated an adult cohort (25+ years) in the USA receiving insulin or sulphonylurea for intensive glycaemic control, angiotensin-converting enzyme inhibitor (ACE-I) or beta-blocker for hypertension, and pravastatin to reduce serum cholesterol. Costs (in 1997 US dollars) and quality-adjusted life years (QALY) were discounted at 3% per annum. The incremental cost- effectiveness ratio (ICER) for intensive glycaemic control was $41,384 per QALY and increased with age at diagnosis. Hypertension control was cost-saving and the ICER for serum cholesterol control was $51,889 per QALY. Using the agents described in this analysis, intensified hypertension control reduced costs and improved clinical outcomes compared to moderate hypertension control, whereas intensive glycaemic control and reduction in serum cholesterol level increased costs and improved health outcomes.

Although modelling data outlining the impact of currently available interventions on glycaemic control, lipid levels and blood pressure, as well on long-term outcomes and costs in type 2 diabetes patients, have been published, to the best of our knowledge there are no published studies looking at the effects of varying the risk factors themselves on long-term outcomes. Assessing the influence of each risk factor (and their effects in combination) may provide insights into those risk factors that should be targeted as a priority when treating type 2 diabetes patients, and thereby taking a step towards treatment optimization.

We designed the present study to evaluate the influence of risk factors of HbA^sub 1c^, lipid levels and systolic blood pressure on the development of complications in individuals with type 2 diabetes. The CORE Diabetes Model was used to calculate the projected effects on life expectancy, quality-adjusted LE (QALE) and total costs of complications of 10% improvements in baseline levels of either total cholesterol (T-CHOL), HDL, systolic blood pressure (SBP), glycosylated haemoglobin (HbA^sub 1c^), and all four parameters combined.

Methods

Model Structure and Simulation Settings

The CORE Diabetes Model, a documented, validated simulation model of type 1 and type 2 diabetes23,24, was used to project life expectancy, QALE and total lifetime costs of diabetes-related complications in a cohort of newly-diagnosed patients with type 2 diabetes similar to those in the United Kingdom Prospective Diabetes Study (UKPDS)11. The CORE Diabetes Model has been previously validated against 66 published studies, including external (third- order) validation of simulations of type 2 diabetes24. The following baseline cohort characteristics were used in the present analysis: mean age 52 years, HbA^sub 1c^. 9.1%, SBP 137 mmHg, T-CHOL 212 mg/ dL, and HDL 39 mg/dL. The following complications were modelled: cardiovascular disease (angina, congestive heart failure, myocardial infarction, peripheral vascular disease and stroke), renal disease, foot ulcer/neuropathy/amputation, and eye disease (retinopathy, cataract and macular oedema).

Transition probabilities, costs of complications and health state utilities/event disutilities that were used in the model have been detailed elsewhere23. Simulations were performed assuming no change in risk factors, assuming patients were receiving hypothetical interventions that led to individual 10% improvements in each risk factor (HbA^sub 1c^, SBP, T-CHOL and HDL), or a 10% improvement in all four risk facto\rs simultaneously.

In the simulation it was assumed that 45.6% of patients were on antiplatelet therapy16, 74% had dilated eye examinations, 55% had urine albumin screening, and 87% had a foot examination at their last visit19. A third-party reimbursement perspective was taken. Costs of complications were expressed in 2003 values in the US setting, and have been outlined elsewhere23. Only direct medical costs of complications were included in the analysis. Costs of day- to-day management of diabetes were not included, nor were indirect costs.

Time Horizon

The simulation was run over a lifetime horizon, in accordance with current guidelines that recommend that the time horizon should be sufficient to capture the development of all relevant complications25.

Discount Rate

Costs were discounted at 3% annually25. Clinical outcomes (life expectancy and quality-adjusted life expectancy) were not discounted. Sensitivity analysis was performed on discounting by varying the discount rate for costs and clinical outcomes between 0 and 6%.

Results

A 10% change in all four risk factors (HbA^sub 1c^, SBP, T-CHOL and HDL), individually and in combination, improved life expectancy and QALE compared to the base-case analysis (Table 1). The base- case simulation produced a mean ( standard deviation) life expectancy of 14.19 0.22 years and QALE of 9.75 0.15 years. Mean total costs over the base-case patient’s lifetime were $83,666 3,086. HbA^sub 1c^ had the single greatest influence on outcomes, followed by SBP, T-CHOL and HDL. A 10% reduction in HbA^sub 1c^ was associated with improvement in QALE of 0.81 0.20 years and saved $10,800 4,030 of total lifetime costs compared to base case. A 10% improvement in SBP similarly improved QALE (0.55 0.19 years) and was cost saving ($7,048 3,961) versus base case. A 10% decrease in T-CHOL was associated with improved QALE (0.20 0.17 years) and increased total costs ($1,923 3,951) compared to base case. A similar pattern was associated with a 10% increase in HDL, with an improvement in QALE of 0.18 0.16 years and an increase in total costs of $2,162 3,415 versus base case. The combination of improved risk factors (-10% HbA^sub 1c^, -10% SBP, -10% T-CHOL and +10% HDL) produced a substantial increase in QALE of 1.27 0.21 years and a saving in total lifetime costs of $14,533 4,099 compared to the base-case analysis.

A breakdown of lifetime costs of complications per patient and the influence of each risk factor showed that a 10% improvement in HbA^sub 1c^ level had the greatest individual impact across a variety of complications (Table 2). A 10% improvement in HbA^sub 1c^ level led to savings in the costs associated with all four complication groups (cardiovascular disease, renal disease, ulcer/ neuropathy/ amputation, eye disease). The greatest cost saving with improved HbA^sub 1c^ levels was $10,758 associated with the reduced incidence of renal disease. SBP improvement also led to cost savings in all four complication groups. Greatest costs savings with a 10% improvement in SBP were associated with decreased renal disease ($4,478). Substantial savings were also observed in the costs of cardiovascular disease ($2,799). Improvements in T-CHOL and HDL levels were associated with savings in the costs associated with cardiovascular disease of $2,251 and $711 respectively. Cost increases associated with renal disease, ulcer/neuropathy/ amputation and eye disease were observed with both of the improvements in lipid risk factors (T-CHOL and HDL). Combined improvements in all four risk factors resulted in cost savings in all four groups of complications. The most substantial savings were seen in renal disease ($12,585) and cardiovascular disease ($3,063).

Table 1. Impact of changes in HbA^sub 1c^, lipids, and blood pressure on long-term outcomes in type 2 diabetes patients

Table 2. Changes in lifetime costs of complications per patient broken down by complication for each risk factor compared to base case

Sensitivity analysis of discount rates on costs and clinical outcomes showed no notable impact on the relative outcomes of the study (i.e. life expectancy, QALE and costs in the improved HbA^sub 1c^, SBP, T-CHOL, HDL and combined improvement groups relative to the base-case analysis and each other).

Discussion

The study showed that 10% improvements in HbA^sub 1c^, SBP, T- CHOL and HDL, individually and in combination, are likely to improve length and quality of life. The improvements were most marked when all four risk factors improved by 10%. Individually, improved HbA^sub 1c^ was associated with greatest gains in life expectancy and QALE, followed by SBP, T-CHOL and HDL. This pattern was also reflected in costs/cost savings.

Hypothetical lipid interventions (i.e. those improving T-CHOL or HDL by 10%) only had an impact on cardiovascular outcomes, and therefore had a smaller overall impact on LE and QALE compared to improvements in HbA^sub 1c^ or SBP, which influence the development of a broader range of complications. This influence was also seen on the total costs of complications, which increased when only T-CHOL or HDL were improved, but decreased when HbA^sub 1c^, SBP and a combination of risk factors were improved. With isolated T-CHOL and HDL improvements, patients lived longer due to decreased incidence of cardiovascular events, but this improvement in life expectancy led to an increased time exposure to the risk of developing other complications like nephropathy, retinopathy and neuropathy. Therefore the total costs over a patient’s lifetime were increased due to these other complications.

HbA^sub 1c^, had an impact on the development of neuropathy and therefore root ulcer/amputation, and the annual incidence of these complications was decreased. This led to an increase in life expectancy, which resulted in an increased cumulative incidence of these events and therefore higher associated lifetime costs. This effect was not seen with improved SBP because of the additional impact of blood pressure on the development of peripheral vascular disease as well as on neuropathy, and hence an even greater overall impact on the development of diabetic ulcers and subsequent amputations. Life expectancy and QALE were improved to the greatest extent, and overall complication costs were reduced the most, with a hypothetical intervention resulting in a 10% improvement in all of the risk factors studied. The most substantial reductions in costs were associated with a lower incidence of renal disease and cardiovascular disease.

These data are supported by those published previously by Gaede et al. from the Steno-2 study26. In a population with type 2 diabetes and microalbuminuria, a targeted, intensified, multifactorial intervention was associated with decreased HbA^sub 1c^, SBP, T-CHOL and triglyceride levels compared to conventional treatment after mean 7.8 years of follow-up. Multifactorial intervention was also associated with a significantly lower risk of cardiovascular disease, renal disease, eye disease and neuropathy compared to conventional therapy. The authors concluded that a target-driven, long-term, intensified intervention aimed at multiple risk factors reduced the risk of cardiovascular and microvascular events by approximately 50% compared to a conventional approach.

A potential criticism of the present study is that, in the analysis of costs, we only included the costs of complications, and no costs associated with the hypothetical interventions were evaluated. Therefore, the costs associated with improved risk factor simulations may be slightly underestimated, and the analysis could potentially underestimate the costs of a treatment that extends life (longer course of treatment). However, the aim of this study was to evaluate the influence of improvements in risk factors, rather than specific interventions, on clinical outcomes and costs in patients with type 2 diabetes. Separate analyses would be required for future interventions (including intervention costs) that improve the risk factors described in this study. A key assumption in this modelling analysis, which should be taken into account when considering the findings in a clinical context, was the persistence of the 10% improvements in risk factors over time. It should also be noted that compliance, drop-outs and potential side effects were not included in the analysis as it was an investigation of the effect of change in risk factors and not a specific intervention. In the real-life situation, with an intervention that improves HbA^sub 1c^, SBP, HDL and/or T-CHOL levels, these factors may play an important role in terms of estimating costs and outcomes.

Conclusions

While interventions that target isolated risk factors were projected to improve LE and QALE, a multifactorial approach was predicted to have the largest impact on these outcomes and to lead to the greatest reduction in the lifetime costs of complications.

Acknowledgements

This study was funded by an unrestricted grant from Eli Lilly Corporation, Indianapolis, Indiana, USA.

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Andrew J. Palmer(a), Stphane Rozea, William J. Valentine(a), Michael E. Minshall(b), Clarice Hayes(c), Alan Oglesby(c) and Giatgen A. Spinas(d)

a COPIE – Center for Outcomes Research, Binningen/Basel, Switzerland

b CORE-USA, LLC, Fishers, Indiana, USA

c Eli Lilly and Company Inc., Indianapolis, Indiana, USA

d Department of Endocrinology, University Hospital Zurich, Switzerland

Address for correspondence: Dr Andrew J. Palmer, CORE – Center for Outcomes Research, Buendtenmattstrasse 40, 4102 Binningen/ Basel, Switzerland; Tel.: +41 61 383 0756; Fax: +41 61 383 0759; email: ap@thecenter.ch

Copyright Librapharm Aug 2004