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O R I G I N A L
A R T I C L E
Mortality and Predictors of Mortality in a
Cohort of Brazilian Type 2 Diabetic
Patients
GIL F. SALLES, PHD1
KATIA V. BLOCH, PHD2
CLAUDIA R.L. CARDOSO, PHD1
OBJECTIVE — To investigate mortality rates and predictors of mortality in Brazilian type 2
diabetic patients.
RESEARCH DESIGN AND METHODS — A prospective follow-up study was carried
out with 471 type 2 diabetic outpatients. Primary end points were all-cause, diabetes-related,
and cardiovascular deaths. Excess mortality in this cohort was evaluated by calculating standardized mortality ratios (SMRs) in relation to those of the Rio de Janeiro population. Predictors
of mortality were assessed by Kaplan-Meier survival curves and by uni- and multivariate Cox
survival analyses.
RESULTS — During a median follow-up of 57 months (range 2– 84 months), 121 (25.7%)
patients died, 91 (75.2%) from diabetes-related causes and 44 (36.4%) from cardiovascular
diseases. After adjusting for age and sex, the all-cause SMR was 3.36 (95% confidence interval
[CI] 2.81– 4.02) and the cardiovascular SMR was 3.28 (CI 2.44 – 4.41). In the Cox multivariate
analysis, the predictors of mortality were older age, increased 24-h proteinuria, preexisting vascular
disease, presence of frequent ventricular premature contractions and prolonged maximum heart
rate⫺corrected QT interval on baseline electrocardiogram, and decreased serum HDL cholesterol.
The use of ␤-blockers was a protective factor. In Kaplan-Meier curves, these variables were capable of
distinguishing subgroups of patients with significantly different prognoses.
CONCLUSIONS — Brazilian type 2 diabetic patients had a more than threefold excess
mortality than the general population, largely because of increased cardiovascular mortality risk.
Several clinical, laboratory, and electrocardiographic predictors of mortality were identified that
could possibly be modified to decrease the mortality burden of type 2 diabetes in Brazil.
Diabetes Care 27:1299 –1305, 2004
S
everal studies have established that
mortality rates of diabetic patients
greatly exceed those of nondiabetic
subjects and that vascular disease occurs
far more frequently in diabetic than in
nondiabetic patients (1– 8). These reports
have included different countries and decades and various sampling schemes.
A Brazilian countrywide survey carried out in 1986 –1988 (9) showed that
the overall diabetes prevalence was 7.6%
and was increased to 17.4% in older sub-
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
From the 1Department of Internal Medicine, Clementino Fraga Filho University Hospital, Medicine Faculty,
Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; and the 2Department of Preventive Medicine,
Clementino Fraga Filho University Hospital, Medicine Faculty, Federal University of Rio de Janeiro, Rio de
Janeiro, Brazil.
Address correspondence and reprint requests to Gil F. Salles, PhD, Rua Croton 72, Jacarepaguá, CEP:
22750-240, Rio de Janeiro, Brazil. E-mail: [email protected].
Received for publication 31 October 2003 and accepted in revised form 8 March 2004.
Abbreviations: DBP, diastolic blood pressure; ECG, electrocardiogram; PVC, premature ventricular
contraction; QTcmax, maximum heart rate⫺corrected QT interval duration; QTd, QT interval dispersion;
SBP, systolic blood pressure; SMR, standardized mortality ratio; WHO, World Health Organization.
A table elsewhere in this issue shows conventional and Système International (SI) units and conversion
factors for many substances.
© 2004 by the American Diabetes Association.
DIABETES CARE, VOLUME 27, NUMBER 6, JUNE 2004
jects, similar to what was found in other
countries. However, data on diabetes
mortality is lacking. Diabetes is well
known to be underreported on death certificates either as an underlying or a contributing cause of death (10). Cohort
studies are the best way to assess mortality
risk in diabetic patients (7). As far as we
know, only one Brazilian prospective investigation of mortality in Japanese migrants has been reported (11).
We have previously reported on the
prognostic factors for total fatal and nonfatal cardiovascular events (12) and
stroke (13) in this cohort. Therefore, the
purpose of the present analysis was to address the impact of type 2 diabetes on allcause and cardiovascular mortality as
compared with that of the background
population and also to investigate the potential predictors of all-cause and diabetesrelated mortality, with particular emphasis
on electrocardiographic abnormalities.
RESEARCH DESIGN AND
METHODS — Characteristics of the
study patients have been detailed elsewhere (12,14). Briefly, all adult diabetic
outpatients (diagnosed according to 1985
World Health Organization [WHO] criteria) who had standard electrocardiograms
(ECGs) recorded from July 1994 to June
1996 were consecutively enrolled in the
study. After applying clinical and electrocardiographic exclusion criteria, as previously described (12,14), the total cohort
numbered 471 type 2 diabetic patients.
The study protocol complied with the
1975 Declaration of Helsinki and was approved by the local ethics committee.
The baseline procedures and criteria
for diagnosing clinical variables have
been previously described (14). All subjects were given a thorough clinical examination, with special attention to signs
and symptoms of cardiovascular diseases
and diabetic degenerative complications.
Laboratory evaluation included fasting
glycemia, fructosamine, serum creatinine, triglycerides, total and HDL cholesterol, and 24-h proteinuria. Mean values
1299
Mortality in Brazilian type 2 diabetic patients
Table 1—Baseline characteristics of survivors and nonsurvivors and results of univariate Cox survival analysis
n
Clinical variables
Sex (male)
Age (years)
Diabetes duration (years)
Retinopathy
Nephropathy
Neuropathy
Arterial hypertension
Heart failure
Coronary heart disease
Cerebrovascular disease
Peripheral vascular disease
SBP (mmHg)
DBP (mmHg)
Diabetes treatment
Sulphonylureas
Insulin
Antihypertensive treatment
␤-Blockers
Calcium channel blockers
Diuretics
ACE inhibitors
Laboratory variables
Fasting glycemia (mmol/l)
Fructosamine (mmol/l)
S-creatinine (␮mol/l)
S-triglycerides (mmol/l)
S-total cholesterol (mmol/l)
S-HDL cholesterol (mmol/l)
Proteinuria (g/24 h)
Electrocardiographic variables
LV hypertrophy
Conduction disturbances
Frequent PVCs
Ischemia or fibrosis
Heart rate (bpm)
QTcmax (ms1/2)
QTcmax ⱖ470( ms1/2)
QTd (ms)
QTd ⬎70 ms
Survivors
Nonsurvivors
Hazard ratio (95% CI)
350
121
—
111 (31.7)
59.5 (41.6–76.0)
7.0 (1.0–22.8)
71 (20.3)
44 (12.6)
43 (12.3)
199 (56.9)
9 (2.6)
36 (10.3)
18 (5.1)
22 (6.3)
140 (115–178)
80 (70–102)
50 (41.3)
65.0 (42.2–80.8)
10.0 (1.0–25.4)
24 (19.8)
26 (21.5)
25 (20.7)
72 (59.5)
11 (9.1)
20 (16.5)
8 (6.6)
21 (17.4)
140 (111–193)
80 (68–105)
1.37 (0.90–2.09)
1.49 (1.21–1.84)*
1.04 (1.02–1.07)*
0.97 (0.58–1.60)
2.16 (1.33–3.49)†
1.68 (1.01–2.78)‡
1.24 (0.81–1.90)
5.44 (2.88–10.27)*
1.98 (1.18–3.31)†
2.19 (1.06–4.52)‡
3.00 (1.77–5.10)*
1.16 (1.05–1.28)†
1.08 (0.89–1.31)
208 (59.4)
94 (26.9)
66 (54.5)
36 (29.8)
1.04 (0.68–1.58)
1.06 (0.67–1.68)
34 (9.7)
66 (18.9)
81 (23.1)
95 (27.1)
2 (1.7)
28 (23.1)
30 (24.8)
40 (33.1)
0.26 (0.06–1.06)
1.46 (0.92–2.33)
1.36 (0.87–2.15)
1.59 (1.04–2.45)‡
9.94 (5.88–18.38)
4.19 (3.00–5.67)
79.60 (61.90–123.80)
1.73 (0.86–3.73)
5.38 (3.62–7.58)
1.09 (0.67–1.51)
0.15 (0.03–2.19)
9.81 (5.23–17.76)
4.28 (2.82–5.89)
88.40 (61.90–259.01)
1.68 (0.88–5.02)
4.91 (3.10–7.83)
1.04 (0.48–1.56)
0.30 (0.04–7.70)
1.00 (0.95–1.05)
1.03 (0.83–1.28)
1.004 (1.002–1.01)*
1.17 (1.01–1.36)‡
1.04 (0.87–1.24)
0.26 (0.12–0.57)*
1.26 (1.17–1.35)*
44 (12.6)
45 (12.9)
17 (4.9)
19 (5.4)
75 (58–107)
436.40 (377.33–498.65)
58 (16.6)
50.0 (28.7–80.0)
56 (16.0)
22 (18.2)
25 (20.7)
14 (11.6)
8 (6.6)
83 (58–107)
440.00 (378.19–532.05)
31 (25.6)
50.0 (24.0–80.0)
25 (20.7)
1.73 (1.04–2.86)‡
1.26 (0.74–2.17)
2.39 (1.24–4.63)†
1.89 (0.92–3.92)
1.22 (1.07–1.39)†
1.08 (1.02–1.14)†
1.90 (1.21–2.99)†
1.11 (0.99–1.24)
1.58 (0.99–2.54)
Data are absolute number (frequency percentage) or median (5–95% percentile) unless otherwise indicated. *P ⬍ 0.001, †P ⬍ 0.01, ‡P ⬍ 0.05. LV, left ventricular.
of all office systolic blood pressure (SBP)
and diastolic blood pressure (DBP) measurements and laboratory examinations
performed in the first year of follow-up
were recorded. Arterial hypertension was
diagnosed for mean SBP ⱖ140 mmHg,
DBP ⱖ90 mmHg, or if antihypertensive
drugs had been prescribed.
From standard resting 12-lead ECGs,
abnormalities were registered according
to the Minnesota code, except left ventricular hypertrophy, which was verified by
1300
voltage criteria (either Sokolow-Lyon or
Cornell sex specific). Premature ventricular contractions (PVCs) were considered
frequent if present in ⬎10% of all cycles
recorded. The ECG intervals were measured as previously reported (14) by a single independent observer. The maximum
heart rate⫺corrected QT interval duration (QTcmax) and QT interval dispersion
(QTd; difference between maximum and
minimum QT intervals) were recorded.
To assess reproducibility, 45 randomly
chosen ECGs were analyzed twice, with at
least 6 months between the measurements. Intraobserver mean relative errors
were 1.1% for QTcmax and 12% for QTd.
Follow-up and end points
The patients were evaluated regularly at
least two times a year until June 2001.
Those who failed to present at the hospital
were contacted annually to determine vital status. Causes of death during the follow-up period were ascertained from
DIABETES CARE, VOLUME 27, NUMBER 6, JUNE 2004
Salles, Bloch, and Cardoso
medical records, death certificates, and
interviews with attending physicians and
families, using a standard questionnaire
reviewed by an independent observer.
Causes of death were coded according to
the International Classification of Diseases.
We defined diabetes-related mortality as
that caused by infection, renal failure, or
cardiovascular causes. Cardiovascular
mortality was defined as death from any
cardiac, cerebral, aortic, or peripheral
vascular disease. The observation period
for each patient was the number of
months from the date of the measured
ECG to the date of death or 30 June 2001.
Overall, 43 (9.1%) patients were lost from
follow-up and were considered as censored observations at the date of their last
hospital visit.
Figure 1—Kaplan-Meier estimation of diabetes-related survival curves in patients grouped according to increased 24-h proteinuria (A), the presence of preexisting peripheral vascular disease
(PVD) or cerebrovascular disease (CVD) (B), and older age (C).
DIABETES CARE, VOLUME 27, NUMBER 6, JUNE 2004
Statistical analysis
Statistics were performed using the
STATA version 7.0 software. Continuous
data were described as medians and
5–95% percentiles. The comparison of
mortality between this cohort and the
population of Rio de Janeiro (using the
midpoint 1996 population) was made by
calculating the standardized mortality ratios (SMRs). The 95% confidence intervals (CIs) for age- and sex-adjusted SMRs
were calculated under Poisson assumption. The Kaplan-Meier estimation of survival curves (compared by log-rank tests)
and uni-and multivariate proportional
hazards Cox models were used for survival analysis. Serum creatinine was log10
transformed because of its positive
skewed distribution. Data for 24-h proteinuria (25%) and HDL cholesterol
(32%) were frequently missing. Deleting
subjects with a missing value on one predictor variable included in multivariate
models commonly leads to biased results
and surely to loss of power (15). Therefore, to decrease bias and increase statistical efficiency, we imputed missing data
using the expectation maximization
method. Variables with a P ⬍ 0.10 in Cox
univariate analysis entered the multivariate models. Sex was forced into the multivariate analysis. To remain in the
multivariate models, a value of P ⬍ 0.10
was necessary. Different multivariate
models were fitted in a forward stepwise
strategy for all-cause and diabetes-related
mortality. Assumptions of the proportional hazards models and interactions
were tested (16), and no violation or significant interaction was observed. Results
1301
Mortality in Brazilian type 2 diabetic patients
are presented as the hazard ratio with
95% CI. A two-tailed P ⬍ 0.05 was considered statistically significant.
RESULTS
Baseline characteristics and followup deaths
After a median follow-up of 57 months
(range 2– 84 months), 121 (25.7%)
patients died, 91 (75.2%) from diabetesrelated causes: 44 (36.4%) from cardiovascular diseases, 40 (33.1%) from
infections, and 7 (5.8%) from renal
failure. The most frequent nondiabetes⫺related cause of death was cancer (13 patients, 10.7%). Table 1 shows
the baseline characteristics of survivors
and deceased patients. Nonsurvivors
were older and had a longer duration of
diabetes, a higher prevalence of cardiovascular diseases and diabetes complications, and more electrocardiographic and
laboratory abnormalities than did survivors. Patients lost from follow-up had
baseline characteristics identical to those
who completed follow-up.
Survival analysis
Table 1 also shows the results of univariate Cox analysis for diabetes-related mortality. Figures 1 and 2 show Kaplan-Meier
survival curves for patients grouped according to the presence or absence of six
prognostically important variables. Table
2 shows the predictive multivariate Cox
regression models for all-cause and diabetes–related mortalities. The independent
mortality predictors were older age, increased 24-h proteinuria, preexisting
peripheral vascular disease (and cerebrovascular disease for diabetes-related
deaths), frequent PVCs and QTcmax prolongation ⬎470 ms1/2 on baseline ECGs,
and a lower serum HDL cholesterol. The
use of ␤-blockers was a protective factor
for mortality.
Figure 2—Kaplan-Meier diabetes-related survival curves in patients grouped according to the
presence of frequent PVCs on baseline ECGs (A), decreased serum HDL cholesterol levels (B), and
the presence of QTcmax prolongation (C).
1302
Standardized mortality ratios
Table 3 shows all-cause and cardiovascular SMRs for patients stratified according
to sex and age ranges. Type 2 diabetic patients had a more than threefold excess
mortality adjusted for age and sex than
the background population of Rio de Janeiro. Although the increased mortality
persisted in both sexes until age 79 years,
it was most prominent in age ranges ⬍70
years, largely because of an increased cardiovascular mortality risk.
DIABETES CARE, VOLUME 27, NUMBER 6, JUNE 2004
Salles, Bloch, and Cardoso
Table 2—Results of multivariate Cox survival analyses
All-cause mortality
Age (10 years)
Peripheral arterial disease (present vs. absent)
24-h proteinuria (1 g/24 h)
Frequent PVCs on ECG (present vs. absent)
S-HDL cholesterol (1 mmol/l)
Use of ␤-blockers (present vs. absent)
QTc max ⱖ470 ms1/2 (present vs. absent)
Diabetes-related mortality
24-h proteinuria (1 g/24 h)
Peripheral arterial disease (present vs. absent)
Age (10 years)
Frequent PVCs on ECG (present vs. absent)
S-HDL cholesterol (1 mmol/l)
QTc max ⱖ470 ms1/2 (present vs. absent)
Use of ␤-blockers (present vs. absent)
Cerebrovascular disease (present vs. absent)
CONCLUSIONS — This prospective
study with up to 7 years of follow-up had
two main findings. First, Brazilian type 2
diabetic patients had a more than threefold excess mortality compared with the
general population. Second, some clinical/demographic (older age, preexistent
vascular disease), laboratory (increased
24-h proteinuria, decreased HDL cholesterol), and electrocardiographic variables
(presence of frequent PVCs, prolonged
QTcmax) were independent predictors of
this increased mortality.
Hazard ratio
95% CI
P
1.52
2.89
1.17
3.36
0.29
0.16
1.61
1.26–1.84
1.79–4.68
1.09–1.26
1.82–6.21
0.15–0.59
0.04–0.67
1.05–2.45
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
0.011
0.029
1.22
3.42
1.53
3.49
0.27
1.74
0.23
2.22
1.13–1.32
1.98–5.89
1.22–1.91
1.69–7.24
0.12–0.61
1.08–2.81
0.06–0.92
1.04–4.74
⬍0.001
⬍0.001
⬍0.001
⬍0.001
0.001
0.023
0.038
0.039
This is, to the best of our knowledge,
the second study on the impact of type 2
diabetes on mortality in Brazil and the
first to report its predictive factors. We
found sex- and age-adjusted SMRs to be
higher than those in investigations from
most other countries (1,4 – 6), but similar
to some (7,17). A report from the WHO
Multinational Study of Vascular Disease
in Diabetes (2) has shown that overall
SMRs vary widely among different cities
and countries, from as low as 1.38 in men
and 1.26 in women in Tokyo, Japan, to as
high as 3.70 in men and 4.35 in women in
Havana, Cuba. However, SMRs from distinct centers should be interpreted cautiously, as the figures might not be
directly quantitatively comparable because of different background mortality
rates (2). Of note was the finding that in
our cohort, the excess mortality was most
important in age ranges ⬍69 years and
was largely explained by an increased cardiovascular mortality. The excess allcause mortality persisted until the 8th
decade of life. These findings are supported by those from other studies
(4,7,17). Also, we did not demonstrate
any difference in diabetes-related mortality risk between sexes, confirming the assumption that the presence of diabetes
equalizes mortality risks between men
and women (8).
Some of the predictors of mortality
observed in this analysis are wellestablished risk factors in patients with
type 2 diabetes, such as older age
(5,6,18,19), preexisting vascular disease
(1,6,19 –21), increased proteinuria
(1,5,6,19,22), and decreased HDL cholesterol (21–23). Two electrocardiographic variables, the presence of
frequent PVCs and maximum QTc interval prolongation, were shown to add
prognostic information for overall survival beyond these traditional risk markers. Frequent PVCs on ECGs have been
demonstrated in population-based stud-
Table 3—All-cause and cardiovascular SMRs for type 2 diabetic patients stratified by age and sex after up to 7 years of follow-up
All-cause mortality
Age ranges (years)
Men
⬍50
50–59
60–69
70–79
ⱖ80
Total
Women
⬍50
50–59
60–69
70–79
ⱖ80
Total
Total adjusted for age and sex
Observed
deaths
Expected
deaths
6
6
17
16
5
50
0.92
2.35
1.31
5.47
4.10
14.15
3
8
31
23
6
71
121
0.61
2.73
2.14
9.15
7.22
21.86
36.01
Cardiovascular mortality
Observed
deaths
Expected
deaths
6.52 (2.93–14.51)*
2.55 (1.15–5.68)‡
12.98 (8.07–20.88)*
2.93 (1.79–4.78)*
1.22 (0.51–2.93)
3.53 (2.68–4.66)*
3
3
5
4
1
16
0.18
0.73
0.44
2.19
1.54
5.09
16.67 (5.38–51.69)*
4.11 (1.33–12.74)†
11.36 (4.73–27.29)*
1.82 (0.68–4.85)
0.65 (0.09–4.61)
3.14 (1.92–5.13)*
4.92 (1.59–15.26)†
2.93 (1.47–5.86)*
14.49 (10.19–20.60)*
2.51 (1.67–3.78)*
0.83 (0.37–1.85)
3.25 (2.58–4.10)*
3.36 (2.81–4.02)*
2
4
16
4
2
28
44
0.17
0.86
0.72
3.54
3.06
8.34
13.43
11.76 (2.94–47.02)*
4.65 (1.75–12.39)*
22.22 (13.61–36.27)*
1.13 (0.42–3.01)
0.65 (0.16–2.60)
3.36 (2.32–4.87)*
3.28 (2.44–4.41)*
SMR (95% CI)
SMR (95% CI)
*P ⬍ 0.001, † P ⬍ 0.05, ‡P ⬍ 0.01
DIABETES CARE, VOLUME 27, NUMBER 6, JUNE 2004
1303
Mortality in Brazilian type 2 diabetic patients
ies to constitute predictors of cardiovascular mortality (24), as was also seen in
this cohort (12). This finding may reflect
myocardial electrical instability or irritability secondary to underlying silent coronary heart disease, possibly related to
the increased risk of sudden arrhythmic
death associated with diabetes and impaired glucose tolerance (25). QTc interval prolongation in diabetes has been
associated with several unfavorable conditions, such as cardiac dysautonomia
(26), underlying coronary heart disease
(27), increased SBP and left ventricular
mass (27), and insulin resistance (28).
Therefore, it is not unexpected that QTc
interval prolongation, probably reflecting
the conjunction of these disadvantageous
conditions, constituted a risk marker for
all-cause and diabetes-related mortality,
as we have previously found for stroke
(13) and cardiovascular events (12). The
prognostic value of QTc interval prolongation for overall mortality risk stratification is supported by two other reports
(20,22). Unlike one of these studies (22),
we were unable to demonstrate any predictive value of QTd for all-cause mortality.
The use of ␤-blocker drugs as a protective factor for mortality was rather unexpected. ␤-Blockers have been showed
to decrease mortality in patients with
heart failure and coronary heart disease,
especially after myocardial infarction.
Hence, ␤-blockers are probably the antihypertensive drug of choice in diabetic
patients with established cardiovascular
disease (29). In the present cohort, only
36 patients (7.6%) were using ␤-blockers
and only 2 of them died. Because of this
small number of patients, the protective
effect of this class of drug on mortality in
diabetic patients should be interpreted
cautiously and further assessed in future
studies.
Two generally accepted mortality
predictors in patients with diabetes, SBP
levels (3,5,18,21) and glycemic control
(1,3,5,19,21), were not selected in this
study. SBP was a predictor of diabetesrelated mortality in univariate Cox analysis, but was not chosen as an independent
predictor in multivariate analysis. Possibly the prognostic information given by
blood pressure levels was incorporated by
the QTcmax, given the strong relation between QTc duration and SBP that has
been demonstrated (27). An alternative
explanation is the influence of antihypertensive treatment on blood pressure lev1304
els, as nearly all the hypertensive patients
were on drug treatment and blood pressure levels were mean values obtained
during the first year of follow-up. With
regard to glycemic control, neither fasting
glycemia nor serum fructosamine showed
any prognostic value for mortality. Because HbA1c levels were not available in
this study, it is possible that metabolic
control status was not adequately evaluated. Another explanation may be that the
median follow-up period of nearly 5 years
could have been too short to demonstrate
the effects of poor glycemic control on
survival, as the occurrence and development of micro- and macrovascular complications that ultimately lead to death
depends not only on the quality of metabolic control, but also on time for their
progression.
Some limitations of this study must
be pointed out. Besides the absence of information about HbA1c levels previously
discussed, other potentially important
prognostic variables were also missing,
such as microalbuminuria and smoking
status. Microalbuminuria is an established risk factor for mortality in diabetic
patients (1,6,19), but its prognostic information could possibly have been replaced
by 24-h proteinuria. Subjects’ smoking
statuses were considered to be nonreliable data in this cohort and were not included. Smoking status has been reported
as a mortality risk factor in some investigations (3,18), but not in others
(6,19,21), so its prognostic importance is
debatable. Another possible flaw was the
study patients’ selection. Because our cohort was hospital based, the diabetic patients in this investigation may not have
been representative of the general type 2
diabetic population of Rio de Janeiro.
Nevertheless, the excess mortality ratios
observed, as previously discussed, were
comparable with those reported in population-based studies from other countries.
Finally, the different methods used to ascertain the causes of death might have introduced some variability into the
accuracy of the information, although the
main source was death certificates (available in more than 90% of the deceased
patients), and the other methods were
mainly confirmatory.
In conclusion, this prospective study
with follow-up for up to 7 years provided
evidence that Brazilian type 2 diabetic patients have a more than threefold excess
mortality than the background popula-
tion; this excess mortality was most important in subjects younger than 69 years
and was partially explained by an increased cardiovascular mortality. Furthermore, the independent predictors of
mortality were older age, preexisting vascular disease, increased 24-h proteinuria,
decreased serum HDL cholesterol, and
frequent PVCs and QTc interval prolongation on baseline ECGs. The use of
␤-blockers appeared to be protective.
These characteristics could help identify
high-risk diabetic patients. Additional investigations with multifactorial interventions are needed to verify if these risk
markers can be modified and hence used
to reduce the burden of mortality in type
2 diabetic patients.
References
1. Kuiman MW, Welborn TA, Whittall DE:
An analysis of excess mortality rates for
persons with non-insulin-dependent diabetes mellitus in Western Australia using
the Cox proportional hazards regression
model. Am J Epidemiol 135:638 – 648,
1992
2. Wang S-L, Head J, Stevens L, Fuller JH:
Excess mortality and its relation to hypertension and proteinuria in diabetic patients: the World Health Organization
Multinational Study of Vascular Disease
in Diabetes. Diabetes Care 19:305–312,
1996
3. Wei M, Gaskill SP, Haffner SM, Stern MP:
Effects of diabetes and level of glycemia
on all-cause and cardiovascular mortality:
the San Antonio Heart Study. Diabetes
Care 21:1167–1172, 1998
4. De Marco R, Locatelli F, Zoppini G, Verlato G, Bonora E, Muggeo M: Cause-specific mortality in type 2 diabetes: the
Verona Diabetes Study. Diabetes Care 22:
756 –761, 1999
5. Chen KT, Chen CJ, Fuh MM-T, Narayan
KMV: Causes of death and associated factors among patients with non-insulin-dependent diabetes mellitus in Taipei,
Taiwan. Diabetes Res Clin Pract 43:101–
109, 1999
6. Florkowski CM, Scott RS, Coope PA,
Moir CL: Predictors of mortality from type
2 diabetes mellitus in Canterbury, New
Zealand: a ten-year cohort study. Diabetes
Res Clin Pract 53:113–120, 2001
7. Weiderpass E, Gridley G, Nyrén O, Pennello G, Landström AS, Ekbom A: Causespecific mortality in a cohort of patients
with diabetes mellitus: a populationbased study in Sweden. J Clin Epidemiol
54:802– 809, 2001
8. Hu FB, Stampfer MJ, Solomon CG, Liu S,
Willett WC, Speizer FE, Nathan DM,
DIABETES CARE, VOLUME 27, NUMBER 6, JUNE 2004
Salles, Bloch, and Cardoso
9.
10.
11.
12.
13.
14.
15.
Manson JAE: The impact of diabetes mellitus on mortality from all-causes and coronary heart disease in women: 20 years of
follow-up. Arch Intern Med 161:1717–
1723, 2001
Malerbi DA, Franco LJ: Multicenter study
of the prevalence of diabetes and impaired
glucose tolerance in the urban Brazilian
population aged 30 – 69 yr. Diabetes Care
15:1509 –1516, 1992
Fuller JH, Elford J, Goldblatt P, Adelstein
AM: Diabetes mortality: new light on an
underestimated public health problem.
Int J Epidemiol 29:140 –148, 2000
Gimeno SG, Ferreira SR, Franco LJ, Iunes
M, Osiro K: Increase in the mortality associated with the presence of diabetes
mellitus in Japanese-Brazilians. Rev Saude
Publica 32:118 –124, 1998
Cardoso C, Salles G, Deccache W: Prognostic value of QT interval parameters in
type 2 diabetes mellitus: results of a longterm follow-up prospective study. J Diabetes Complications 17:169 –178, 2003
Cardoso CRL, Salles GF, Deccache W:
QTc interval prolongation is a predictor of
future strokes in patients with type 2 diabetes mellitus. Stroke 34:2187–2194,
2003
Cardoso C, Salles G, Bloch K, Deccache
W, Siqueira-Filho AG: Clinical determinants of increased QT dispersion in patients with diabetes mellitus. Int J Cardiol
79:253–262, 2001
Greenland S, Finkle WD: A critical look at
methods for handling missing covariates
in epidemiologic regression analysis. Am J
DIABETES CARE, VOLUME 27, NUMBER 6, JUNE 2004
Epidemiol 142:1255–1264, 1995
16. Concato J, Feinstein AR, Holford TR: The
risk of determining risk in multivariate
models. Ann Intern Med 118:201–210,
1993
17. Berger B, Stenström G, Sundkvist G: Incidence, prevalence, and mortality of diabetes in a large population: a report from
Skaraborg Diabetes Registry. Diabetes
Care 22:773–777, 1999
18. Ford ES, DeStefano F: Risk factors for
mortality from all-causes and from coronary heart disease among persons with diabetes. Am J Epidemiol 133:1220 –1230,
1991
19. Gall MA, Borch-Johnsen K, Hougaard P,
Nielsen FS, Parving H-H: Albuminuria
and poor glycemic control predict mortality in NIDDM. Diabetes 44:1303–1309,
1995
20. Christensen PK, Gall MA, Major-Pedersen
A, Sato A, Rossing P, Breum L, Pietersen
A, Kastrup J, Parving HH: QTc interval
length and QT dispersion as predictors of
mortality in patients with non-insulin-dependent diabetes. Scand J Clin Lab Invest
60:323–332, 2000
21. Ostgren CJ, Lindblad U, Melander A, Rastan L: Survival in patients with type 2 diabetes in a Swedish community: Skaraborg
Hypertension and Diabetes Project. Diabetes Care 25:1297–1302, 2002
22. Sawicki PT, Kiwitt S, Bender R, Berger M:
The value of QT interval dispersion for
identification of total mortality risk in
non-insulin-dependent diabetes mellitus.
J Intern Med 243:49 –56, 1998
23. Laakso M, Lehto S, Penttilä I, Pyörälä K:
Lipids and lipoproteins predicting coronary heart disease mortality and morbidity in patients with non-insulin-dependent
diabetes. Circulation 88:1421–1430, 1993
24. Knutsen R, Knutsen SF, Curb JD, Reed
DM, Kautz JA, Yano K: The predictive
value of resting electrocardiograms for
12-year incidence of coronary heart disease in the Honolulu Heart Program.
J Clin Epidemiol 41:293–302, 1988
25. Curb JD, Rodriguez BL, Burchfiel CM,
Abbott RD, Chiu D, Yano K: Sudden
death, impaired glucose tolerance and diabetes in Japanese American men. Circulation 91:2591–2595, 1995
26. Whitsel EA, Boyko EJ, Siscovick DS: Reassessing the role of QTc in the diagnosis
of autonomic failure among patients with
diabetes. Diabetes Care 23:241–247,
2000
27. Festa A, D’Agostino R Jr, Rautaharju P,
Mykkänen L, Haffner SM: Relation of systemic blood pressure, left ventricular
mass, insulin sensitivity, and coronary artery disease to QT interval duration in
nondiabetic and type 2 diabetic subjects.
Am J Cardiol 86:1117–1122, 2000
28. Dekker JM, Feskens EJM, Schouten EG,
Klootwijk P, Pool J, Kromhout D: QTc
duration is associated with levels of insulin and glucose tolerance: the Zutphen Elderly Study. Diabetes 45:376 –380, 1996
29. Mooradian AD: Cardiovascular disease in
type 2 diabetes mellitus: current management guidelines. Arch Intern Med 163:33–
40, 2003
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