Pre-existing cardiovascular diseases and glycemic control in patients with type 2 diabetes mellitus in Europe: a matched cohort study
OPrrigein-al einxveisstitgaitniogn cardiovascular diseases and glycemic control in patients with type 2 diabetes mellitus in Europe: a matched cohort study
Alex Z Fu 0
Ying Qiu 1
Larry Radican 1
Donald D Yin 1
Panagiotis Mavros 1
0 Department of Quantitative Health Sciences, Cleveland Clinic , Cleveland, Ohio , USA
1 Global Outcomes Research, Merck & Co., Inc., Whitehouse Station , New Jersey , USA
Background: Although there is a growing body of evidence showing that patients with type 2 diabetes mellitus (T2DM) have poor glycemic control in general, it is not clear whether T2DM patients with pre-existing cardiovascular diseases (CVD) are more or less likely to have good glycemic control than patients without pre-existing CVD. Our aim was to examine the degree of glycemic control among T2DM patients in Europe with and without pre-existing CVD. Methods: This is a matched cohort study based on a multi-center, observational study with retrospective medical chart reviews of T2DM patients in Spain, France, United Kingdom, Norway, Finland, Germany, and Poland. Included patients were aged >= 30 years at time of diagnosis of T2DM, had added a SU or a PPAR agonist to failing metformin monotherapy (index date) and had pre-existing CVD (cases). A control cohort with T2DM without pre-existing CVD was identified using 1:1 propensity score matching. With difference-in-difference approach, logistic and linear regression analyses were applied to identify differences in glycemic control by CVD during the follow up period, after controlling for baseline demographics, clinical information, and concurrent anti-hyperglycemic medication use. Results: The percentage of case patients with adequate glycemic control relative to control patients during the 1st, 2nd, 3rd, and 4th years after the index date was 19.9 vs. 26.5, 16.8 vs. 26.5, 18.8 vs. 28.3, and 16.8 vs. 23.5 respectively. Cases were significantly less likely to have adequate glycemic control (odds ratio: 0.62; 95% confidence interval: 0.460.82) than controls after adjusting for baseline differences, secular trend, and other potential confounding covariates. Conclusions: T2DM patients with pre-existing CVD tended to have poorer glycemic control than those without preexisting CVD, all other factors being equal. It suggests that clinicians may need to pay more attention to glycemic control among T2DM patients with CVD.
Patients with type 2 diabetes mellitus (T2DM) are at an
increased risk of developing vascular complications.
Cardiovascular diseases (CVD) are a major concern
considering that the risk of cardiovascular death in patients with
T2DM is double the risk of individuals without diabetes
[1,2]. Patients with diabetes also have the same risk of
cardiovascular death as patients with a history of
myocardial infarction and no diabetes [1,3].
The literature does not seem to show a universally
consistent relationship between glycemic control and CVD,
despite the documented beneficial effect of glycemic
control on microvascular complications [4-6]. The
metaanalysis conducted by Selvin and colleagues  reviewed
13 prospective cohort studies, and the pooled results
indicated that chronic hyperglycemia was associated with
cardiovascular disease in patients with T2DM. Another
meta-analysis  based on 8 randomized controlled trials
found a similar relationship and concluded that glycemic
control reduces the incidence of cardiovascular events in
T2DM. A prospective epidemiological analysis based on
the Heart Outcome Prevention Evaluation (HOPE) study
also identified a significant relationship between glycemic
level and incident cardiovascular events . Most
recently, Ray et al conducted a meta-analysis of five
prospective randomized controlled trials and the results
indicated that intensive glycemic control has
cardiovascular benefits compared with standard treatment for
individuals with T2DM .
In contrast, results from several other published studies
suggest that additional research is necessary to further
clarify the relationship between glycemic control and
CVD [11,12]. The ACCORD (Action to Control
Cardiovascular Risk in Diabetes) trial with 3.5-years follow-up
found that the use of intensive therapy for glycemic
control in patients with T2DM did not reduce cardiovascular
events but increased mortality compared to standard
therapy . Meanwhile, the ADVANCE (Action in
Diabetes and Vascular Disease: Preterax and Diamicron
Modified Release Controlled Evaluation) trial with
5years follow-up also did not find a significant reduction in
cardiovascular events in T2DM patients with intensive
treatment for glycemic control compared to patients with
standard therapy . Similarly, the results from another
retrospective cohort study suggest that there is little or no
relationship between glycemic level and recurrent
cardiovascular events . Clearly, it is still early to claim a
definitive monotonic association between glycemic
control and CVD, and additional research is warranted.
While studying this hyperglycemia-CVD relationship
in T2DM, existing studies have been primarily focused on
the impact of glycemic control on CVD outcomes. Few
studies have addressed this relationship from a different
angle. Although there is a growing body of evidence
showing that patients with T2DM have poor glycemic
control in general, it is not clear whether T2DM patients
with pre-existing CVD are more or less likely to have
good glycemic control than patients without pre-existing
CVD. The purpose of the current study was to examine
the degree of glycemic control among T2DM patients in
Europe with and without pre-existing CVD.
The Real-Life Effectiveness and Care Patterns of Diabetes
Management (RECAP-DM) study is a European
multicenter based, epidemiological and naturalistic
observational study for patients with T2DM. Using retrospective
clinical chart review and patient survey at the point of
visit, the RECAP-DM study was conducted in clinical
practice settings in seven European countries including
Spain, France, United Kingdom, Norway, Finland,
Germany, and Poland. At the beginning of the study, a mailed
invitation was sent to randomly selected physicians
asking if they would be willing to participate in the study.
The participating physicians included endocrinologists,
diabetologists, general practitioners, and internalists.
The RECAP-DM included patients aged 30 years or
older at time of diagnosis of T2DM, who had added a SU
or PPAR agonist (glitazones) to failing metformin
monotherapy on a date from January 2001 to January
Baseline Period Study Period
(6 months prior to Index Date) (From Index Date to patient Visit Date)
(Initial addition of a sulphonylurea or glitazone to metformin monotherapy)
Figure 1 Diagram for patient selection.
2006, which was defined as the index date (Figure 1).
Patient enrollment in the RECAP-DM occurred during
regular visits within the period from June 2006 to
February 2007. Eligible patients were required to have at least
one hemoglobin A1c (HbA1c) measurement in the
12months prior to the visit date. Patients were excluded
from the RECAP-DM if they had type 1 diabetes, were
pregnant women with gestational diabetes, or had
diabetes secondary to other factors (such as malnutrition,
infection, and surgery). Patients who were unable to
complete the questionnaires or were participating in other
clinical studies were excluded as well. To our knowledge,
RECAP-DM is the only study applying a consistent
methodology across multiple European countries focusing on
patients with T2DM who also received combination oral
diabetes medications [15,16].
Study Design and Variables
The current study was a matched cohort study using the
RECAP-DM sample. The case cohort included those who
had pre-existing CVD (i.e., with onset date prior to the
index date) within the RECAP-DM and the control
cohort was selected among those who had no
pre-existing CVD. CVD included ischemic heart disease,
myocardial infarction (MI), stroke, and peripheral vascular
disease based on ICD-9 codes. Patient baseline
information was collected and included patient demographic
characteristics and clinical information during the
6months prior to the index date.
The primary outcome of focus was HbA1c at baseline as
well as during the study period (from index date to
patient visit date - Figure 1). Another outcome of interest
was the proportion of patients with adequate glycemic
control, defined as HbA1c < 6.5% by the 2005
International Diabetes Federation . To evaluate the pattern of
glycemic control over time, patients were grouped based
on time, categorized in years from the index date to the
visit date. For each year, only samples with HbA1c
measures within the year were included. If there were
multiple HbA1c measures within the year for a patient, the
most recent measurement relative to the visit date was
Other baseline covariates that were controlled for in the
analysis included age, gender, ethnicity, duration of
T2DM, alcohol consumption status, physical activity
frequency, country location, body mass index, physician
specialty, and duration of metformin use. The
participating physicians included endocrinologists, diabetologists,
general practitioners, and internalists for RECAP-DM.
Endocrinologists or diabetologists may be more acutely
aware of disease management needs relative to general
practitioners or internalists, which could influence the
glycemic outcomes of their patients. Thus, a classification
of endocrinologists/diabetologists versus general
practitioners/internalists was applied for physician specialty.
The index medication use of SU versus glitazones was
controlled for as well.
During the study period, patients may not be persistent
in their use of the index medication. The treatment
pattern could change, which would confound the evaluation
of the relationship between baseline CVD and the HbA1c
at various time. Therefore, the concurrent
anti-hyperglycemic medication treatment at the time of the HbA1c
measurement was also captured and controlled for in the
analysis. If the date of the HbA1c measurement was
between the starting and stop dates of a certain
antihyperglycemic medication, that medication was defined
as the concurrent treatment at the time of that HbA1c
measurement. In the analyses, the concurrent treatment
was categorized into 6 groups: metformin+SU
combination therapy, metformin+glitazones combination therapy,
SU monotherapy, glitazones monotherapy, metformin
monotherapy, and therapies with insulin. There might be
slight overlap between certain treatment types, such as
the combination therapy and therapies with insulin.
Thus, the estimated effect on any treatment type
reflected the marginal difference between patients with
that therapy and those without any of the 6
Descriptive analysis was conducted to summarize patient
demographic characteristics and clinical information at
baseline. The variables were compared between patients
with and without CVD at baseline. T-tests were used for
continuous variables and chi-square tests were calculated
for categorical variables.
The propensity score method was used to match case
and control cohorts. A logistic regression was first used
to predict the probabilities of pre-existing CVD using a
list of baseline characteristics. The variable selection was
based on the idea [18,19] that it was the variables having
an effect on or associated with the HbA1c and blood
glucose instead of CVD that needed to be matched so that
the impact of pre-existing CVD on HbA1c can be properly
evaluated. The variables that did not have an effect on
HbA1c outcomes were not included.
The case and control cohorts were 1:1 matched by
propensity scores using the greedy matching algorithm .
That is, once a control is matched, the control is not
reconsidered. The algorithm makes "best" matches first
and "next-best" matches next, in a hierarchical sequence
until no more matches can be made. Best matches are
those with the highest digit match on propensity score.
First, controls were matched to cases on 8 digits of the
propensity score. For those that did not match, controls
were then matched to cases on 7 digits of the propensity
score. The algorithm proceeds sequentially to the lowest
digit match on propensity score (1 digit).
The trends of glycemic control and the impact of
preexisting CVD on glycemic control were analyzed using a
difference-in-difference (DID) strategy [21,22]. The DID
approach compares the pre- and post-index difference in
glycemic control among T2DM patients with pre-existing
CVD, with the pre- and post-index difference in glycemic
control among T2DM patients without pre-existing
CVD. Thus, the DID strategy allows one to identify the
effect attributable to pre-existing CVD after accounting
for any possible secular trend.
Logistic and linear regression analyses were applied to
assess any relationship between pre-existing CVD and
glycemic control (HbA1c<6.5% yes/no) or HbA1c value.
For the DID approach, 5 additional binary variables were
included in the regression analyses indicating the 5
postindex time periods (<1 year, 1-2 years, 2-3 years, 3-4
years, and 4 years from the index date). These were used
to capture the time effect on outcomes. Five interaction
terms for CVD with the 5 post-index periods were
included in the model, capturing the potential differential
CVD-effects on glycemic control over time.
As a sensitivity analysis, the time effect was assumed
linear after the index date. Thus, the analytical model was
reduced where only 2 time variables were included, one
binary variable indicating the time of post versus
preindex and another continuous variable representing the
actual time from the index date measured in years.
Subsequently, for the sensitivity analysis, 2 interaction terms
were included to capture the potential differential
CVDeffects on glycemic control. Robust variance estimator
was used to account for multiple observations per patient
and correlation within subjects.
In our study, several covariates had missing or
unknown data for certain patients. Although the missing
for each covariate is trivial, the final sample size would
have been reduced to less than half of the total if the
complete case analysis approach was adopted. We used the
multiple imputation procedure to impute missing values
of each covariate, assuming that the data are multivariate
normally distributed and the missing data are missing at
random. The procedure used the Markov Chain Monte
Carlo method with a single chain to create imputations.
This study had 1942 T2DM patients with complete
questionnaire information for CVD at the baseline. There
were 406 patients (20.9%) with CVD and 1536 patients
without CVD before the index date. Among those with
pre-existing CVD, ischemic heart disease was the most
prevalent (272 patients, 67%), followed by MI (103
patients, 25%), peripheral vascular disease (98 patients,
24%), and stroke (47 patients, 12%).
The descriptive comparisons of all the demographics
and baseline clinical information between patients with
and without pre-existing CVD are shown in Table 1.
Compared to patients without CVD at the baseline, those
with CVD were significantly older and more likely to be
male. UK and Poland had more patients whereas Spain
and Finland had fewer patients with pre-existing CVD in
the sample. The percentage of the sample with adequate
glycemic control (HbA1c<6.5%) was significantly lower
for those with pre-existing CVD compared to those
without. There were significantly more patients with
preexisting CVD who added SU instead of glitazones to
metformin monotherapy at the index date compared to
patients without CVD.
Patient characteristics after propensity score 1:1
matching are also listed in Table 1, where the created case and
control cohorts were comparable regarding baseline
characteristics. Of the 394 patients with pre-existing
CVD, 65% were male, mean (standard deviation) age and
duration of T2DM was 64.0 (9.0) and 6.0 (5.0) years,
Figure 2 shows the percentage of the sample with
HbA1c<6.5% (adequate glycemic control) and the average
HbA1c over time. The percentage of the case cohort with
adequate glycemic control relative to the control cohort
during the 1st, 2nd, 3rd, and 4th years after index date
was 19.9 vs. 26.5, 16.8 vs. 26.5, 18.8 vs. 28.3, and 16.8 vs.
23.5 respectively. The sample sizes remained almost
identical between the case and control cohorts although both
decreased over time owing to attrition.
None of the interaction terms between pre-existing
CVD and the post-index time periods in the regression
analyses were statistically significant, indicating no
differential CVD-effects on glycemic control over time. Thus,
the final regression models excluded these interaction
terms. The results from the final regressions are reported
in Table 2 and indicate that patients with pre-existing
CVD were significantly less likely to have adequate
glycemic control (odds ratio: 0.62; 95% confidence interval:
0.46-0.82) than those without pre-existing CVD after
controlling for other potential confounding covariates.
The effect was not significant when HbA1c value was the
outcome of focus. The almost identical results for CVD
between regressions on the same outcome variable
implies that treating the post-index time continuously or
categorically year-by-year had little difference on the
coefficient of interest. All time covariates were
significant, indicating that the likelihood of adequate glycemic
control was higher for post-index outcomes compared to
their pre-index values. This demonstrates the
effectiveness of the post-index medication treatment regardless of
the patient cohorts. Such a finding is important for the
original RECAP-DM study.
Our study results suggest that there is a significant
difference in the percentage of T2DM patients with adequate
glycemic control (HbA1c < 6.5%) in those with and
without pre-existing CVD. Using a seven-country European
sample, the current study provides important empirical
evidence about the degree of glycemic control among
T2DM patients with and without pre-existing CVD. Most
of the previous research has focused on CVD resulting
from poor glycemic control as the outcome of interest.
The present study is unique in that we used HbA1c value
and adequate glycemic control as the outcomes of
interest and studied CVD as a baseline factor. Due to the
design of the RECAP-DM study, the temporal
relationship between CVD and glycemic control is apparent.
The premise of the study is that, other things being
equal, patients with pre-existing CVD should have better
glycemic control due to increased risk of future
cardiovascular events. Our study showed that patients at
increased risk of cardiovascular events are not controlled
any better than patients with lower risk (and in fact their
control is worse). This implies an important unmet
medical need. The reasons for the observed difference in
glycemic controls between the two groups remain a
question. We have adjusted for the concurrent
antihyperglycemic medication use in the analyses. Thus, the
treatment difference might not be a contributor.
However, the literature indicates that physicians taking care of
diabetic patients with CVD might face multiple obstacles
for obtaining adequate glycemic control. Treatment
guidelines suggest more stringent control of blood
pressure and lipids, in addition to the blood glucose control.
There are a variety of additional medications (e.g.,
antiplatelet medications) that are recommended to prevent
future CVD events. More importantly, there is evidence
that glycemic control may not be the most significant
factor for preventing another CVD event [23,24]. Diabetic
patients with CVD may have conditions (e.g., congestive
heart failure) that prevent the use of certain
anti-hyperglycemic agents. Additionally, due to concomitant
condiBaseline Clinical Information
With HbA1c < 6.5%
Body mass index (kg/m2)
Duration of metformin use (years)
Note: CVD: cardiovascular diseases; S.D.: standard deviation.
tions and medication interactions, these patients may be
at particularly high risk for hypoglycemia, which may
cause even more morbidity than mild hyperglycemia in
the short term. Obviously, more research is needed in this
Our analyses have several strengths. First, this study
used the RECAP-DM sample, which was recruited using
a consistent methodology across seven European
counFigure 2 Glycemic control over time.
tries focusing on T2DM patients who received
combination oral diabetes treatment. Second, we applied the DID
strategy in the analysis design. This approach cancelled
out both the secular trend and the baseline group
difference while evaluating HbA1c differences between patients
with and without pre-existing CVD in the follow-up
period. Further, the DID method assumes the
comparison groups exhibit the same trend over time as the null
hypothesis. This assumption holds better when the
baseline difference is small . This is the case for the
current study where no significant difference was observed
for both HbA1c value and the percentage of patients with
adequate glycemic control for the matched samples.
In this study of European patients with T2DM, the
majority of patients had not reached the goal of adequate
glycemic control with HbA1c < 6.5%. This might partially
be explained by the nature of the RECAP-DM sample
which comprised patients who failed metformin
monotherapy. Existing studies based on RECAP-DM  also
showed that more and more patients used therapies with
insulin over time, which indicated intensification of the
medication treatment for this group of T2DM patients. It
is also likely that most of the patients in this sample had
moderate to severe T2DM. As indicated by our study
results, the proportions of patients with adequate
glycemic control decreased over time. This reflected the
progressively deteriorating nature of T2DM, which has been
demonstrated in the literature [25,26]. Unfortunately, due
to lack of data we did not include analyses on dosage
information on the anti-hyperglycemic agents used after
the index date. This could have provided useful
information regarding differences in intensification regimens
between case and control cohorts. Nonetheless, the
suboptimal glycemic control seen in CVD patients could also
have resulted from physicians' fear of increased mortality
With CVD vs. without CVD
Study period vs. baseline
Years after the index
95% confidence intervals
Note: CVD: cardiovascular diseases. Other controlled covariates in the regression analyses: concurrent treatment of the HbA1c measurement
(metformin+SU combination therapy, metformin+glitazones combination therapy, SU monotherapy, glitazones monotherapy, metformin
monotherapy, any therapies with insulin), index SU vs. glitazones, age, gender, Caucasian, duration of diabetes, never used alcohol, physical
activity, country, BMI, physician specialty.
risk, which was observed in the ACCORD trial .
Additionally, using the interaction terms in the regression
analysis, we planned to test whether or not the HbA1c of
patients with CVD tended to deteriorate more quickly
than the HbA1c of patients without CVD. The results
indicated that the differential effect among our sample
was not statistically significant.
Due to the 1:1 propensity score matching, the final
sample (N = 788) was considerably reduced from the
original (N = 1942). A sensitivity analysis with propensity
score 1:3 matching with replacement was conducted (N =
1620) and the results were similar. We decided to use the
1:1 matching as our primary analysis because of the
greater internal validity of the design. Further, an
alternative approach using last observation carried forward was
adopted as a sensitivity analysis, and similar results were
found. It is also worth noting that we applied a multiple
imputation procedure to impute the missing values of
each covariate. Sensitivity analysis excluding patients
with missing data was conducted (complete case analysis)
and as expected, the regression results were numerically
similar but non-significant, which was likely due to the
smaller sample size (N = 430).
Although a noteworthy difference in adequate glycemic
control was identified between T2DM patients with and
without pre-existing CVD, this study has several
limitations. First, this group of selected patients all had SU or
PPAR agonist added to metformin monotherapy, and
they were recruited through their physicians who had
agreed to participate in the RECAP-DM study. These
physicians may be more motivated due to their
willingness to participate and their patients may not represent
the overall population of patients with T2DM in Europe.
Second, the CVD status was collected at baseline.
Patients without CVD before the index date may develop
CVD over time, which leads to potential misclassification
of the CVD status. Nevertheless, this effect can only
lessen the potential difference to be identified. With a
significant finding at present, the true difference in glycemic
control in those with and without pre-existing CVD
would have been larger if the potential misclassifications
were to be considered. Third, the biochemical marker of
microalbuminuria was not collected in the baseline
period. Given that T2DM patients are at significantly
increased risk of cardiovascular events , it would have
been important to control for other key risk factors for
CVD such as microalbuminuria. Fourth, as is typical with
any observational study, there may have been other
unobserved confounding factors not available in the data (e.g.,
other comorbid conditions) that could have led to
Based on a seven-country European sample, we found
that T2DM patients with pre-existing CVD tended to
have poorer glycemic control than those without
preexisting CVD, all other factors being equal. This implies a
need for building awareness, education, and novel
effective (or more aggressive) treatments for T2DM patients
with CVD. Current treatments may not be adequate in
this population. It is widely recognized that achieving
specific glycemic goals in patients with diabetes can
substantially reduce diabetes-related complications. Since
patients with pre-existing CVD have a higher risk of
future diabetes-related complications, clinicians may
want to pay more attention to glycemic control in these
high risk patients.
AZF received research grant support from Merck & Co., Inc. for this study. YQ,
LR, DDY, and PM are employed by and are share holders of Merck & Co., Inc.
All authors contributed to the design of this study. AZF and PM performed the
statistical analyses. All authors contributed to the interpretation of the study
results and writing process, and approved the final manuscript.
1. Haffner SM , Lehto S , Ronnemaa T , Pyorala K , Laakso M : Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction . N Engl J Med 1998 , 339 : 229 - 234 .
2. Saydah SH , Miret M , Sung J , Varas C , Gause D , Brancati FL : Postchallenge hyperglycemia and mortality in a national sample of U .S. adults. Diabetes Care 2001 , 24 : 1397 - 1402 .
3. Malmberg K , Yusuf S , Gerstein HC , Brown J , Zhao F , Hunt D , Piegas L , Calvin J , Keltai M , Budaj A : Impact of diabetes on long-term prognosis in patients with unstable angina and non-Q wave myocardial infarction: results of the Oasis Registry . Circulation 2000 , 102 : 1014 - 1019 .
4. Diabetes Control and Complications Trial (DCCT) Research Group: Effect of intensive diabetes management on cardiovascular events and risk factors in the diabetes control and complications trial . Am J Cardiol 1995 , 75 : 894 - 903 .
5. UK Prospective Diabetes Study (UKPDS) Group: Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) . Lancet 1998 , 352 : 837 - 853 .
6. Ohkubo Y , Kishikawa H , Araki E , Miyata T , Isami S , Motoyoshi S , Kojima Y , Furuyoshi N , Shichiri M : Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin dependent diabetes mellitus: a randomized prospective 6-year study . Diabetes Res Clin Pract 1995 , 28 : 103 - 117 .
7. Selvin E , Marinopoulos S , Berkenblit G , Rami T , Brancati FL , Powe NR , Golden SH : Meta-analysis: glycosylated hemoglobin and cardiovascular disease in diabetes mellitus . Ann Intern Med 2004 , 141 : 421 - 431 .
8. Stettler C , Allemann S , Jni P , Cull CA , Holman RR , Egger M , Krhenbhl S , Diem P : Glycemic control and cardiovascular disease in types 1 and 2 diabetes mellitus: Meta-analysis of randomized trials . Am Heart J 2006 , 152 : 27 - 38 .
9. Gerstein HC , Pogue J , Mann JF , Lonn E , Dagenais GR , McQueen M , Yusuf S , HOPE investigators: The relationship between dysglycaemia and cardiovascular and renal risk in diabetic and non-diabetic participants in the HOPE study: a prospective epidemiological analysis . Diabetologia 2005 , 48 : 1749 - 1755 .
10. Ray KK , Seshasai SR , Wijesuriya S , Sivakumaran R , Nethercott S , Preiss D , Erqou S , Sattar N : Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomised controlled trials . Lancet 2009 , 373 : 1765 - 1772 .
11. Kauffman AB , Delate T , Olson KL , Cymbala AA , Hutka KA , Kasten SL , Rasmussen JR : Relationship between haemoglobin A1C values and recurrent cardiac events: A retrospective, longitudinal cohort study . Clin Drug Investig 2008 , 28 : 501 - 507 .
12. Cefalu WT : Glycemic targets and cardiovascular disease . N Engl J Med 2008 , 358 : 2633 - 2635 .
13. Action to Control Cardiovascular Risk in Diabetes Study Group , Gerstein HC , Miller ME , Byington RP , Goff DC Jr, Bigger JT , Buse JB , Cushman WC , Genuth S , Ismail-Beigi F , Grimm RH Jr, Probstfield JL , Simons-Morton DG , Friedewald WT : Effects of intensive glucose lowering in type 2 diabetes . N Engl J Med 2008 , 358 : 2545 - 2559 .
14. ADVANCE Collaborative Group , Patel A , MacMahon S , Chalmers J , Neal B , Billot L , Woodward M , Marre M , Cooper M , Glasziou P , Grobbee D , Hamet P , Harrap S , Heller S , Liu L , Mancia G , Mogensen CE , Pan C , Poulter N , Rodgers A , Williams B , Bompoint S , de Galan BE , Joshi R , Travert F : Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes . N Engl J Med 2008 , 358 : 2560 - 2572 .
15. Alvarez Guisasola F , Mavros P , Nocea G , Alemao E , Alexander CM , Yin D : Glycaemic control among patients with type 2 diabetes mellitus in seven European countries: findings from the Real-Life Effectiveness and Care Patterns of Diabetes Management (RECAP-DM) study . Diabetes Obes Metab 2008 , 10 (Suppl 1): 8 - 15 .
16. Alvarez Guisasola F , Tof Povedano S , Krishnarajah G , Lyu R , Mavros P , Yin D : Hypoglycaemic symptoms, treatment satisfaction, adherence and their associations with glycaemic goal in patients with type 2 diabetes mellitus: findings from the Real-Life Effectiveness and Care Patterns of Diabetes Management (RECAP-DM) Study . Diabetes Obes Metab 2008 , 10 (Suppl 1): 25 - 32 .
17. International Diabetes Federation : Clinical guidelines task force: global guideline for type 2 diabetes . 2005 [http://www.idf.org/webdata/docs/ IDF%20GGT2D.pdf].
18. Fu AZ , Li L : Thinking of having a higher predictive power for your firststage model in propensity score analysis? Think again . Health Serv Outcomes Res Method 2008 , 8 : 115 - 117 .
19. Brookhart MA , Schneeweiss S , Rothman KJ , Glynn RJ , Avorn J , Sturmer T : Variable selection for propensity score models . Am J Epidemiol 2006 , 163 : 1149 - 1156 .
20. Parsons LS : Performing a 1:N case-control match on propensity score . Proceedings of the 29th SAS Users Group International (SUGI) conference , 2004 : 165 - 29 .
21. Athey S , Imbens GW : Identification and inference in nonlinear difference-in-differences models . Econometrica 2006 , 74 : 431 - 497 .
22. Fu AZ , Dow WH , Liu GG : Propensity score and difference-in-difference methods: a study of second-generation antidepressant use in patients with bipolar disorder . Health Serv Outcomes Res Method 2007 , 7 : 23 - 38 .
23. Vijan S , Hayward RA : Treatment of hypertension in type 2 diabetes mellitus: blood pressure goals, choice of agents, and setting priorities in diabetes care . Ann Intern Med 2003 , 138 : 593 - 602 .
24. Huang ES , Meigs JB , Singer DE : The effect of interventions to prevent cardiovascular disease in patients with type 2 diabetes mellitus . Am J Med 2001 , 111 : 633 - 42 .
25. Turner RC , Cull CA , Frighi V , Holman RR : Glycemic control with diet, sulfonylurea, metformin, or insulin in patients with type 2 diabetes mellitus: progressive requirement for multiple therapies (UKPDS 49) . UK Prospective Diabetes Study (UKPDS) Group. JAMA 1999 , 281 : 2005 - 2012 .
26. Rahier J , Guiot Y , Goebbels RM , Sempoux C , Henquin JC : Pancreatic betacell mass in European subjects with type 2 diabetes . Diabetes Obes Metab 2008 , 10 (Suppl 4): 32 - 42 .
27. Brown WV : Microvascular complications of diabetes mellitus: renal protection accompanies cardiovascular protection . Am J Cardiol 2008 , 102 : 10L - 13L .