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. 2015 Oct;6(5):199–209. doi: 10.1177/2042018815592803

Effects of benchmarking on the quality of type 2 diabetes care: results of the OPTIMISE (Optimal Type 2 Diabetes Management Including Benchmarking and Standard Treatment) study in Greece

Vasilis Tsimihodimos 1, Michael S Kostapanos 2, Alexandros Moulis 3, Nikos Nikas 4, Moses S Elisaf 5,✉
PMCID: PMC4579413  PMID: 26445642

Abstract

Objectives:

To investigate the effect of benchmarking on the quality of type 2 diabetes (T2DM) care in Greece.

Methods:

The OPTIMISE (Optimal Type 2 Diabetes Management Including Benchmarking and Standard Treatment) study [ClinicalTrials.gov identifier: NCT00681850] was an international multicenter, prospective cohort study. It included physicians randomized 3:1 to either receive benchmarking for glycated hemoglobin (HbA1c), systolic blood pressure (SBP) and low-density lipoprotein cholesterol (LDL-C) treatment targets (benchmarking group) or not (control group). The proportions of patients achieving the targets of the above-mentioned parameters were compared between groups after 12 months of treatment. Also, the proportions of patients achieving those targets at 12 months were compared with baseline in the benchmarking group.

Results:

In the Greek region, the OPTIMISE study included 797 adults with T2DM (570 in the benchmarking group). At month 12 the proportion of patients within the predefined targets for SBP and LDL-C was greater in the benchmarking compared with the control group (50.6 versus 35.8%, and 45.3 versus 36.1%, respectively). However, these differences were not statistically significant. No difference between groups was noted in the percentage of patients achieving the predefined target for HbA1c. At month 12 the increase in the percentage of patients achieving all three targets was greater in the benchmarking (5.9–15.0%) than in the control group (2.7–8.1%). In the benchmarking group more patients were on target regarding SBP (50.6% versus 29.8%), LDL-C (45.3% versus 31.3%) and HbA1c (63.8% versus 51.2%) at 12 months compared with baseline (p < 0.001 for all comparisons).

Conclusion:

Benchmarking may comprise a promising tool for improving the quality of T2DM care. Nevertheless, target achievement rates of each, and of all three, quality indicators were suboptimal, indicating there are still unmet needs in the management of T2DM.

Keywords: benchmarking, glycated hemoglobin, low-density lipoprotein cholesterol, quality of care, systolic blood pressure, type 2 diabetes

Introduction

In 2012 it was estimated that approximately 371 million individuals worldwide had diabetes. Of those, 334 million had type 2 diabetes (T2DM) [International Diabetes Federation, 2012a]. The increasing T2DM prevalence can be attributed, at least in part, to the aging of the population together with the current global obesity epidemic [Smyth and Heron, 2007. In Greece, the estimated T2DM prevalence in adults without established vascular disease had been increased by 4.8% in 2001 to 12.8% in 2006 [Panagiotakos et al. 2009]. The age-adjusted 5-year incidence of T2DM was 5.5% [Panagiotakos et al. 2008].

T2DM is a multisystem abnormality with various microvascular and macrovascular complications. Namely, it increases the risk of coronary heart disease (CHD), stroke and disability. Undiagnosed or poorly controlled diabetes can lead to lower limb amputation, blindness and kidney disease. T2DM is also associated with reduced quality of life and productivity and significantly increased healthcare cost [International Diabetes Federation, 2012a; Green et al. 2012]. In this context, it is relevant to optimize the prevention and management of T2DM [Sherwin and Jastreboff, 2012; Freeman, 2010].

Improving glycemia was associated with microvascular risk reduction. Several studies highlighted the protective role of good blood pressure control [Fowler, 2008]. Also, targeting dyslipidemia, hypertension and hyperglycemia together with smoking cessation can reduce the risk of macrovascular complications [Fowler, 2008; Libby and Plutzky, 2002; Rizzo et al. 2013]. Interestingly, data are poor, suggesting an association between improved glycemia and reduced risk of CHD or stroke in patients with T2DM [Libby and Plutzky, 2002].

During recent years several guidelines for the management of T2DM-related abnormalities have been available [International Diabetes Federation, 2012b; American Diabetes Association, 2012]. However, the implementation of these guidelines in clinical practice is problematic [Kiefe et al. 2001]. It was suggested that the control of vascular risk factors, including increased systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C) and glycated hemoglobin (HbA1c) levels, is suboptimal in T2DM. . Several methods have been proposed for improving the quality of diabetes care. These included flowcharting, data collection and analysis as well as feedback [Kiefe et al. 2001; Loveridge, 2005; Merlani et al. 2011].

Feedback is the process by which data on the clinical performance of one physician or clinic are collected during an audit phase. Subsequently these data are sent to the physician or clinic in the form of a report, including proposed clinical interventions or notifications of outcomes needing improvement. Benchmarking is a type of feedback method in which, in addition to the above, all data collected during the audit phase are compared with the clinical performance of other physicians or clinics. In turn, the report is delivered back to the physicians or clinics in a process known as benchmarked feedback [Loveridge, 2005; Nobels et al. 2011]. These methods have been used and studied extensively in various healthcare settings [Kiefe et al. 2001; Loveridge, 2005].

The OPTIMISE (Optimal Type 2 Diabetes Management Including Benchmarking and Standard Treatment) study was performed in six European countries. It tested the hypothesis of whether benchmarking is superior to a nonbenchmarking follow-up strategy in the control of LDL-C, HbA1c and SBP in adults with T2DM. According to baseline data from the global [Hermans et al. 2013] and the Greek study population [Kostapanos et al. 2014] a high percentage of patients with T2DM are poorly controlled regarding LDL-C and HbA1c levels, as well as SBP. Herein, we present the 12-month results of the Greek OPTIMISE study participants. It should be acknowledged that medical practice varies from country to country due to differences in medical education, culture and philosophy, as well as in the availability of resources. Analyzing the Greek results separately may limit the power of the study from a statistical point of view. However, it is relevant to identify potential differences in the study data between the six participating countries. In this context, it is worth presenting the results separately for Greece. In this respect, any differences in the Greek cohort characteristics or results compared with the rest of the study participants could provide valuable information regarding the clinical practice in Greece.

Methods

Study design

The OPTIMISE study [ClinicalTrials.gov identifier: NCT00681850] was an international, multicenter, prospective cohort study performed in six European countries (Belgium, Greece, UK, Spain, Luxembourg and Portugal). Herein, the data from the Greek participants of this study are presented. Final global study results have been published elsewhere [Hermans et al. 2013]. The study design, including the inclusion/exclusion criteria, and the global baseline results, have been described in detail [Hermans et al. 2013]. Briefly, the study included patients aged at least 18 years with T2DM [defined as fasting plasma glucose (FPG) ⩾ 6.99 mmol/liter (126 mg/dl) and 2 h postload plasma glucose ⩾ 11.1 mmol/liter (200 mg/dl) in two separate blood samples (taken on separate days)], treated (including insulin treatment) or untreated. These patients were followed for 12 months according to routine clinical practice, every 4 months. At each visit, clinical and laboratory characteristics were recorded. Patients with type 1 diabetes, gestational diabetes or those admitted to the hospital for diabetes control during the study period were excluded.

In Greece, 84 physicians were randomized 3:1 to the benchmarking feedback group (n = 61) or the control group (n = 23). Every 4 months, the benchmarking group of physicians was given feedback on the level of control of HbA1c, SBP and LDL-C and target achievement rates for their patients compared with other physicians’ patients. In contrast, the control group was not given any feedback. Both the benchmarking and the control group received feedback on the results of outcome indicators for their patients [HbA1c, FPG, total cholesterol (TC), LDL-C, high-density lipoprotein cholesterol (HDL-C), triglycerides (TG)]. A figure illustration of an example of the benchmarking provided to OPTIMISE physicians has been presented elsewhere [Nobels et al. 2011].

Primary and secondary objectives

The primary objective of this study was to evaluate the impact of benchmarking on the quality of care for patients with T2DM. This was assessed by the percentage of patients of the benchmarking group versus the control group achieving predefined targets for three critical quality indicators (i.e. SBP, HbA1c and LDL-C) after a 12-month follow up.

The hypothesis of whether using benchmarking improves the quality of patient care, particularly in controlling glycemia, lipid abnormalities and hypertension, was also tested. In this context, the percentages of patients achieving predefined targets for HbA1c, LDL-C and SBP after 12 months of follow up versus baseline or the change in these parameters at 12 months were determined.

The predefined treatment targets were as follows: HbA1c less than 7%; SBP less than 130 mmHg and less than 125 mmHg for patients with significant proteinuria (>1 g/24 h); and LDL-C less than 2.59 mmol/liter (100 mg/dl) and less than 1.81 mmol/liter (70 mg/dl) for very high-risk patients (i.e. those with diabetes and CHD). These targets were based on the concurrent guidelines of the American Diabetes Association, the 4th Joint Task Force of the European Society of Cardiology and other relevant societies on cardiovascular disease prevention at the time of study initiation [Buse et al. 2007; Graham et al. 2007; American Diabetes Association, 2007].

Clinical and laboratory evaluation

The methods used for the assessment of clinical and laboratory parameters in the OPTIMISE study have been previously described [Hermans et al. 2013]. At each visit blood samples were collected after an overnight fast. These were sent to a central laboratory (Bio Analytical Research Corporation, Ghent, Belgium), which determined the following parameters: HbA1c, FPG, TC, HDL-C, LDL-C and TG levels.

Statistical methods

The statistical analysis of the OPTIMISE study data has previously been described [Nobels et al. 2011]. Namely, descriptive analysis of continuous variables included mean 95% confidence intervals on the mean (CI), standard deviation, and the number of available observations. The descriptive analysis of qualitative variables included the numbers and percentages for each of the scores or categories, and the number of observations.

These descriptions were performed on all evaluable patients, being split by group: benchmarking/control. Comparisons between the two treatment groups were performed using mixed or repeated-measure models adapted to cluster randomized data. The statistical analysis was performed using the SAS software, version 9.2 (SAS Institute, Inc, Cary, NC, USA). The statistical significance level was set at 0.05. Herein we describe only data for the participants in Greece. This study represents a nonprespecified subgroup analysis of the global OPTIMISE study.

Results

Demographics and patient characteristics

Between September 2008 and January 2009, all participating physicians enrolled a total of 797 patients with diabetes: 570 in the benchmarking group and 227 in the control group. The last patient’s final visit took place in January 2010. The dropout rate at 12 months was 17.0% (97/570) in the benchmarking group and 5.3% (12/227) in the control group. The main reason for dropout was patient switching to another physician, accounting for 88.6% of the dropouts in the benchmarking group, and for 91.7% in the control group. Patients in the benchmarking and the control groups were age and sex matched (Table 1). The vast majority of participants in both groups had hypertension: 77.7% in the benchmarking versus 76.2% in the control group) (Table 1). CHD was more prevalent in the benchmarking than in the control group (25.6 versus 19.1%, respectively). Baseline characteristics of the Greek OPTIMISE population were published in detail elsewhere [Kostapanos et al. 2014]. Patients’ lifestyle at baseline and at 12 months is presented in Table 2. For both the benchmarking and the control group, the proportion of patients who had no or light physical activity was decreased at 12 months compared with baseline.

Table 1.

Baseline characteristics and relevant medical history of the study population.

Characteristics Benchmarking group
 Control group
mean ± SD (n = 570) mean ± SD (n = 227)
Age, years 64.2 ± 10.5 62.9 ± 11.3
Men (%) 328 (57.5) 129 (56.8)
Age at diagnosis, years 54.4 ± 11.4 53.6 ± 10.5
Time since diagnosis, years 9.4 ± 8.6 8.8 ± 7.4
Relevant medical history % %
Hypertension 77.7 76.2
Coronary heart disease 25.6 19.1
Peripheral arterial disease 10.8 11.8
Stroke 6.7 5.3
Retinopathy 6.1 9.9
Known proteinuria 6.3 3.7
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SD, standard deviation.

Table 2.

Lifestyle characteristics at baseline and 12 months.

Patients Benchmarking group
 Control group
Baseline (n = 570) Month 12 (n = 473) Baseline (n = 227) Month 12 (n = 215)
Smokers, % 24.2 25.6 24.7 26.5
No or light physical activity, % 78.6 74.6 74.5 70.2
BMI, kg/m2 (mean ± SD) 29.6±4.9 29.2±5.0 29.6±5.0 29.5±5.0
Waist circumference, cm (mean ± SD) 102.9±13.6 100.2±13.5 102.0±13.4 100.8±12.9
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BMI, body mass index; SD, standard deviation.

At 12 months the percentage of patients on lipid-lowering drugs was slightly increased in both the benchmarking and control groups from baseline. The relevant percentage was raised only for aspirin in the control group (Table 3). Slight decreases were noted in the percentage of patients on antiobesity treatment in the control group (Table 3). However, no significant differences were noted in body mass index at 12 months (Table 2).

Table 3.

Medications prescribed in patients of the benchmarking and control groups at baseline and 12 months.

Benchmarking group, %
 Control group, %
Baseline Month 12 Baseline Month 12
Antidiabetics 92.3 92.6 94.3 94.9
Lipid-lowering agents 71.8 79.5 63.4 68.8
Antihypertensive agents 95.9 95.3 96.5 97.6
Aspirin 39.0 42.9 33.5 41.4
Drug treatment for obesity 4.7 3.4 5.7 2.3
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In both groups, SBP was decreased at month 12 compared with baseline: mean SBP 137.4 versus 128.8 mmHg respectively in the benchmarking group and 140.7 versus 135.3 mmHg respectively in the control group. This decrease was greater in the benchmarking group compared with the control group. Also, the mean HbA1c was lowered at month 12 compared with baseline in both groups: mean HbA1c 7.2% versus 6.7% respectively in the benchmarking group and 7.0% versus 6.8% respectively in the control group). LDL-C was reduced in both the benchmarking and the control groups. However, the 95% CIs for the decrease in the control group were markedly wider, crossing 0. Namely, LDL-C decreased from 2.9 to 2.54 mmol/liter (112 to 98 mg/dl) in the benchmarking group [mean difference −0.31 mmol/liter (95% CI −0.39 to −0.23 mmol/liter)] and from 2.9 to 2.82 mmol/liter (112 to 109 mg/dl) in the control group [mean difference −0.11 mmol/liter (95% CI −0.26 to 0.03 mmol/liter)].

Target achievement for SBP, HbA1c and LDL-C

At month 12, 50.6% of benchmarking group patients had achieved the SBP target compared with 35.8% of the control group patients at the same time (p = 0.073) and with 29.8% of the benchmarking group patients at baseline (p < 0.001, Figure 1).

Figure 1.

Figure 1.

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Patients (%) of the benchmarking and control group achieving the systolic blood pressure (SBP) target (i.e. SBP < 130 mmHg and < 125 mmHg for patients with known proteinuria) at baseline, 4, 8 and 12 months.

*p < 0.001 for the percentage of patients of the benchmarking group within the SBP target at month 12 versus baseline; #p = 0.073 for the percentage of patients of the benchmarking versus the control group within the SBP target at month 12.

No difference in the percentage of patients having achieved the HbA1c target was noted between the benchmarking and the control group at month 12: 63.9% vs 62.8%, respectively. In the benchmarking group a greater percentage of patients achieved the HbA1c target at 12 months compared with baseline (63.9 vs 51.2%, respectively, p < 0.001, Figure 2).In the same group, a greater percentage of patients achieved the LDL-C target at 12 months as compared with baseline (45.3 vs 31.3%, respectively, p < 0.001, Figure 3). No statistically significant difference was noted between the percentages of patients of the benchmarking and the control group on target for LDL-C levels at 12 months (Figure 3).

Figure 2.

Figure 2.

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Patients (%) of the benchmarking and control group achieving the glycated hemoglobin (HbA1c) target (i.e. HbAlc < 7%) at baseline, 4, 8 and 12 months.

*p < 0.001 for the percentage of patients of the benchmarking group within the HbAlc target at month 12 versus baseline; #p = 0.947 for the percentage of patients of the benchmarking versus the control group within the HbA1c target at month 12.

Figure 3.

Figure 3.

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Patients (%) of the benchmarking and control group achieving the low-density lipoprotein cholesterol (LDL-C) target (i.e. <100 mg/dl and <70 mg/dl for patients with diabetes and CHD) at baseline, 4, 8 and 12 months.

*p < 0.001 for the percentage patients of the benchmarking group within the LDL-C target at month 12 versus baseline; #p = 0.110 for the percentage of patients of the benchmarking versus the control group within the LDL-C target at month 12.

The percentages of patients who achieved two or three of the aforementioned targets at 0, 4, 8 and 12 months are shown in Figure 4. Increased percentages of patients who achieved two or three targets were noted in both the benchmarking and the control group after 12 months. However, at 12 months, the percentages of patients who achieved two or all three targets was greater in the benchmarking group (37% and 15% respectively) compared with the control group (32.2% and 8.2% respectively).

Figure 4.

Figure 4.

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Patients (%) of (a) the benchmarking and (b) the control group achieving none, one, two or all three targets (systolic blood pressure, glycated hemoglobin and low-density lipoprotein cholesterol) at baseline, 4, 8 and 12 months.

Discussion

The OPTIMISE study assessed the effect of benchmarking on the quality of care in patients with T2DM. This study enrolled a total of 3996 evaluable patients with T2DM, with more than half of them (54.5%) in Belgium. Of the remaining five European participating countries, the study population of Greece was the largest (n = 797), accounting for 19.9% of the overall population.

The OPTIMISE baseline results of Greece suggested that despite increased proportions of patients on antidiabetic, lipid-lowering and antihypertensive treatment, target achievement is still poor [Kostapanos et al. 2014]. These findings highlight the need for improvement in the quality of care of adults with T2DM.

It must be acknowledged that since this study was designed almost a decade ago predefined treatment targets, especially those for blood pressure and HbA1c, were relatively aggressive. Recent guidelines for the treatment of patients with type 2 diabetes suggest more flexible and individualized treatment targets. It can be hypothesized that using such goals could have resulted in target achievement by a greater number of patients with diabetes in the OPTIMISE study. However, target-guided intervention cannot overcome problems that undermine treatment success, including lack of resources, poor compliance, delays in treatment intensification etc.

Benchmarking is a form of feedback not only providing information on the level of control of SBP, HbA1c and LDL-C, but also comparing each physician’s performance with that of his peers. It adds to the benefit of feedback by strongly motivating for change of the participating physicians [Kiefe et al. 2001; Hayes and Ballard, 1995]. Benchmarking provides an opportunity for many physicians to have access to their relative performance, capitalizing on the physicians’ existing intrinsic motivation. Despite this added benefit, there have not been many studies to evaluate the potential benefits of benchmarking feedback in T2DM.

A systematic review of the literature included 10 studies assessing the impact of feedback (not of benchmarking) on the quality of T2DM care [Guldberg, 2009]. An improvement of the quality of care was associated with using feedback. Namely, the frequency of testing (i.e. increased number of eye exams, foot exams and HbA1c measurements) was raised. However, this change results in no benefit on outcomes, including the achieved targets used as endpoints in the OPTIMISE study [Olivarius et al. 2001; Philips et al. 2005; O’Connor et al. 2011].

No studies have used benchmarking for the improvement of quality of T2DM care [Kiefe et al. 2001; Hunt et al. 2009; Schroll et al. 2012; O’Connor et al. 2009]. According to the Greek OPTIMISE results, using benchmarking was not associated with an increased percentage of patients achieving the predefined target for HbA1c compared with the control group at the end of the study. A trend towards a higher percentage of patients in the benchmarking group achieving the SBP target than in the control group was noted. However, this difference was not statistically significant. Similarly, a nonsignificantly higher percentage of patients achieved the LDL-C target in the benchmarking group compared with the control group at the end of the study. The relatively small population of the study in the Greek region might not have the statistical power to demonstrate the significance of these differences. In contrast, the global OPTIMISE study, which included 4027 patients, showed significantly increased percentages of patients within the SBP (40.0 versus 30.1%, p < 0.001) and the LDL-C target (54.3 versus 49.7%, p = 0.006) in benchmarking patients compared with controls. This difference was not relevant for the HBA1c target (58.9% versus 62.1%, p = 0.398) [Hermans et al. 2013].

To date, all published studies using benchmarking [Kiefe et al. 2001; Hunt et al. 2009; Schroll et al. 2012; O’Connor et al. 2009] have assessed the change in the percentages of patients achieving targets over the study follow up only exceptionally [Hunt et al. 2009]. In the present study, the percentage of patients in the benchmarking group achieving the SBP target was significantly increased at 12 months. This increase is in accordance with the results of the global study [Hermans et al. 2013]. Similar increases were relevant in the percentages of patients in the benchmarking group who achieved the HbA1c and LDL-C target at 12 months compared with baseline. These results were also consistent with those of the global study [Hermans et al. 2013].

Two studies assessed the impact of benchmarking on the levels (but not on the percentage of patients) of critical quality indicators [Hunt et al. 2009; O’Connor et al. 2009]. A randomized study following patients for 12 months reported decreased LDL-C and HbA1c levels after 12 months of follow up in both the physician-intervention group and the control group. This difference was not relevant between the intervention and the control group [O’Connor et al. 2009]. In a study including 6072 patients, using benchmarking for 24 months significantly improved LDL-C but not HbA1c levels [Hunt et al. 2009]. According to the results of the present study a decrease in the HbA1clevels may also be expected.

The OPTIMISE results for Greece in accordance with the global study [Hermans et al. 2013] suggest benefits of benchmarking on not only the percentages of patients achieving treatment targets, but also in the mean HbA1c, SBP and LDL-C levels. However, the percentages of patients achieving the targets at 12 months still remain unsatisfactory. Namely, only 15% of the patients in the benchmarking group achieved all three targets at month 12 versus 5.9% at baseline. The relevant percentages from the global data were 12.5% versus 5.2%. The percentages of patients in the benchmarking group achieving all three targets at 12 months were higher than those in the control group (15.0% versus 8.2% respectively). In the global study, 8.1% of patients in the control group achieved all three targets at month 12 [Hermans et al. 2013]. These results are also in agreement with the results of a randomized trial including 8405 adult patients with T2DM. In this study, 12.6% of patients in the intervention group achieved all three targets after 12 months compared with 8.5% of those in the control group [Peterson et al. 2008]. However, in this study intervention was somehow different, including audit and feedback, creation of a registry, patient reminders of targets, appointments and education, but not comparison with peers (i.e. benchmarking). Although benchmarking seems to be an interesting concept, it can be further improved with the use of individualized treatment targets, the establishment of rewards (not necessarily financial) for best practicing physicians and the inclusion of cost-related parameters (e.g. total cost, use of generic drugs).

One of the strong points of the OPTIMISE study is its prospective cohort study design, including randomization at the physician level. This design provides a control for physician bias that could be introduced if the same physician was to follow both benchmarking and control patients. Furthermore, assigning physicians to receive benchmarking in a random manner controlled for physician selection bias. Also, using a control group at the physician level minimized confounders arising from potential differences in behavioral standards towards patient care and changes in T2DM management over the study period. Additionally, data were compared at the patient level, providing adjustment for the fact that certain patients could have been individually chosen to adopt a healthier lifestyle (i.e. dietary habits and increased exercise). Other strengths of this study include the participation of multiple sites located throughout Greece, its relatively long duration, the comprehensive and documented inventory of measures with transparency of clinical performance, and the use of a central laboratory for the measurement of all critical quality indicators.

Nevertheless, this study’s results should be interpreted by considering several limitations. Since the current analysis of the Greek OPTIMISE data is a nonprespecified subgroup analysis, its statistical power to identify the differences between the study groups is limited. Also, the impact of benchmarking in physicians’ practicing attitude may have introduced sizable within-group variability. Besides, the frequency of determining quality indicators was predefined by the protocol at intervals of about 4 months. Although this mimics standard practice as suggested by guidelines, it may have contributed to increased ‘patient adherence’ to the visit plan and improvement of the goal attainment rates for all participants. Furthermore, since physicians in this study were only office based, the results may not be generalizable to the entire Greek healthcare setting consisting of both office-based and hospital-based physicians. Also, participating in a study may introduce another level of bias, as the physicians have an additional motivating factor to improve performance. This may explain at least in part the improvements noted in the control group. Finally, a peculiar finding in this Greek substudy was the higher dropout rate in the benchmarking group. Unfortunately, no explanation other than the chance to drop out can be provided for this difference between study groups.

In conclusion, benchmarking was associated with improved achievement of SBP, LDL-C and HbA1c targets in patients with T2DM at 12 months compared with baseline. However, a significant difference compared with the control group was noted only in the percentage of patients achieving the SBP target. The Greek results of the OPTIMISE study are promising for the role of benchmarking in improving physicians’ performance towards optimal quality of T2DM care. However, high proportions of patients are still off targets. In this context, it is important to identify novel evidence-based therapeutic modalities or interventions for the management of T2DM.

Footnotes

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by AstraZeneca. Medical writing support was provided by Mrs Andriana Papaconstantinou of Qualitis Ltd, funded by AstraZeneca.

Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Moses S. Elisaf and Vasilis Tsimihodimos have given talks, attended conferences and participated in trials sponsored by AstraZeneca. Michael S. Kostapanos has no relevant conflict of interest. Alexandros Moulis and Nikos Nikas are AstraZeneca employees.

Contributor Information

Vasilis Tsimihodimos, Department of Internal Medicine, Medical School, University of Ioannina, Ioannina, Greece.

Michael S. Kostapanos, Department of Internal Medicine, Medical School, University of Ioannina, Ioannina, Greece

Alexandros Moulis, Medical Department, AstraZeneca SA, Athens, Greece.

Nikos Nikas, Medical Department, AstraZeneca SA, Athens, Greece.

Moses S. Elisaf, Professor of Medicine, Department of Internal Medicine, Medical School, University of Ioannina, 451 10 Ioannina, Greece.

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