Original Article
Association of metabolic risk factors with uncontrolled
hypertension: comparison of the several definitions of
metabolic syndrome
Nuno Cortez-Dias a,b,c, Susana R. Martins a,b, Adriana Belo d, Manuela Fiuza a,b,
on behalf of VALSIM Study investigators
Aims: To evaluate the influence of metabolic syndrome in
the effectiveness of antihypertensive treatment and to
compare it using the National Cholesterol Education
Program Adult Treatment Panel III (NCEP-ATP III) (2001 and
2004), International Diabetes Federation (IDF) and
American Heart Association/National Heart, Lung and
Blood Institute (AHA-NHLBI) definitions.
Methods: The VALSIM (Estudo de Prevalência da
Sı́ndrome Metabólica) survey was designed as an
observational cross-sectional study performed in a primary
healthcare setting in Portugal. The first two adult patients
scheduled for an appointment on a given day were invited
to participate. The treatment effectiveness was evaluated
by the occurrence of uncontrolled hypertension (140/
90 mmHg) in patients taking antihypertensive drugs.
Logistic regression analysis was used to determine the
association between uncontrolled hypertension and
metabolic risk factors, with adjustments for age, sex, and
pattern of antihypertensive treatment.
Results: Among the 16 856 individuals evaluated, 8925treated hypertensive patients were identified. Only 35.8%
of them had controlled hypertension. The risk of poor
blood pressure control increased with age, waist
circumference, serum levels of triglycerides and HDLcholesterol. Among treatable risk factors, metabolic
syndrome as defined by NCEP-ATP III 2001 diagnostic
criteria was the strongest independent predictor of
uncontrolled hypertension (odds ratio: 1.23; 95% CI:
1.08–1.41; P ¼ 0.002). In opposition, the IDF or AHANHLBI definitions of metabolic syndrome failed to identify
patients at risk of poor blood pressure control.
Conclusion: Metabolic syndrome is associated with lower
effectiveness of antihypertensive therapy and the NCEPATP III 2001 definition of metabolic syndrome is the one
that better identifies patients at risk of poor blood pressure
control.
Keywords: abdominal obesity, antihypertensive,
hypertension, metabolic syndrome X, obesity, primary
healthcare, treatment effectiveness
Abbreviations: BP, blood pressure; HDL-C, HDLcholesterol
INTRODUCTION
H
ypertension is the most prevalent cardiovascular
risk factor [1]. The increased availability and use of
antihypertensive drugs has enabled significant
reductions in cardiovascular morbidity and mortality [2].
However, hypertension control rates remain poor despite
the large number of drugs usually prescribed [3–6] highlighting the need for further analysis of concomitant factors
that may influence blood pressure (BP) control.
Hypertension is often part of a constellation of cardiovascular risk factors including obesity, abdominal obesity,
atherogenic dyslipidemia, glucose intolerance, and diabetes mellitus. This cluster of risk factors (metabolic risk
factors) in the same individual has been termed metabolic
syndrome and it seems to identify a subgroup of individuals
at higher risk of cardiovascular events [7]. The concept of
metabolic syndrome evolved, leading to the various definitions proposed by different medical associations. The
definitions most often used are those of the National
Cholesterol Education Program Adult Treatment Panel III
(NCEP-ATP III) [8,9], the International Diabetes Federation
(IDF) [10] and the American Heart Association/National
Heart, Lung and Blood Institute (AHA-NHLBI) [11]. Crucial
differences exist between the definitions in the diagnostic
cut-offs for each risk factor, the number of factors required,
and which factors are considered essential. There is considerable disagreement as to which definition is most
clinically appropriate and there are no epidemiological
studies comparing their predictive value for adverse
cardiovascular events.
There is increasing evidence that metabolic abnormalities predict the incidence of hypertension [12], suggesting
Journal of Hypertension 2013, 31:1991–1997
a
Department of Cardiology, Santa Maria University Hospital, bLisbon Medical School,
University of Lisbon, cProgramme for Advanced Medical Education (Fundação Calouste Gulbenkian, Ministry of Health and Foundation for Science and Technology) and
d
Portuguese Society of Cardiology, Lisbon, Portugal
Correspondence to Nuno Cortez-Dias, Rua Professor Moisés Amzalak, n8 14, 78 Fte,
1600-648 Lisbon, Portugal. Tel: +351964175339; fax: +351261864897; e-mail:
[email protected]
Received 27 December 2012 Revised 7 April 2013 Accepted 15 May 2013
J Hypertens 31:1991–1997 ß 2013 Wolters Kluwer Health | Lippincott Williams &
Wilkins.
DOI:10.1097/HJH.0b013e32836342f7
Journal of Hypertension
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Cortez-Dias et al.
that metabolic risk factors might interfere with the mechanisms of hypertension. Several studies suggested that the
individual components of the metabolic syndrome are
associated with higher BP and affect the BP control in
patients taking antihypertensive drugs [13–17]. Furthermore, BP control worsens with the increasing number of
metabolic risk factors associated with hypertension [14]
despite the use of a greater number of medications [18].
Previous studies argued that metabolic syndrome is associated with poor BP control [14,18,19]. However, none of
them determined the specific impact of metabolic syndrome on the risk of uncontrolled hypertension, which
requires its isolation from the effects of the individual risk
factors that are components of the syndrome. Furthermore,
the several definitions of metabolic syndrome were never
compared in respect to the identification of patients at
higher risk of poor BP control.
This study aims to evaluate the influence of metabolic
syndrome in the effectiveness of antihypertensive treatment
and to compare it according to the several definitions of
metabolic syndrome in use.
POPULATION AND METHODS
The VALSIM survey was designed as an observational
cross-sectional survey of Portuguese adult primary healthcare users [20]. The study involved 719 general practitioners, randomly selected based on proportional
distribution by district and region. The study was carried
out between April 2006 and November 2007. The first
two adult patients of each physician’s working day fulfilling the inclusion criteria were asked to participate,
irrespective of the reason for the consultation and of the
presence of cardiovascular risk factors. The inclusion
criterion was the existence of laboratory measurements
of fasting glucose, HDL-cholesterol (HDL-C) and triglycerides collected within the previous 12 months; the exclusion
criterion was the presence of clinical conditions that
could affect the diagnosis of metabolic syndrome, such
as thyroid dysfunction. Each investigator was requested to
provide information for 10–15 patients. After informed
consent, a questionnaire was applied to characterize sociodemographic, anthropometric, clinical, and laboratory
variables. The following assessments were made during
the patient’s visit: weight, height, waist circumference
(at a point midway between the iliac crest and the lower
edge of the ribcage, with the subject standing) and seated
BP (the average of two measurements after a 5-min rest
period).
The present analysis is restricted to the subpopulation of
hypertensive patients under antihypertensive treatment. In
these patients taking antihypertensive drugs, uncontrolled
hypertension was defined as the presence of BP at least
140/90 mmHg. Obesity was defined as BMI at least 30 kg/m2
and overweight as BMI between 25.0 and 29.9 kg/m2.
Abdominal obesity was defined as waist circumference
more than 102 cm for men and more than 88 cm for women.
Hypertriglyceridemia was defined by serum levels at least
150 mg/dl (1.7 mmol/l) and reduced HDL-C by serum levels
less than 40 mg/dl in men (1.03 mmol/l) and less than
50 mg/dl in women (1.29 mmol/l). Diabetes was defined
1992
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as fasting glucose at least 126 mg/dl or bellow if the patient
was taking antidiabetic drugs. Impaired fasting glucose
was defined as fasting glucose more than 110 mg/dl
(6.1 mmol/l) when applying the NCEP-ATP III definition,
and more than 100 mg/dl (5.6 mmol/l) for the other definitions. Taking into account that all patients had hypertension, the diagnosis of metabolic syndrome was made
applying adapted criteria from NCEP-ATP III 2001 [8] and
2004 [9], IDF [10], and AHA-NHLBI [11] definitions, requiring the presence of at least two additional metabolic risk
factors (Table 1).
Statistical analysis
Data are expressed as mean SD. Categorical variables
were analyzed to determine their absolute and relative
frequencies (adjusted for age and sex). Multivariate logistic
regression analysis was used to identify factors associated
with uncontrolled hypertension. The potentially relevant
variables tested in the model were: sex, age, BMI, waist
circumference, triglycerides, HDL-C, dysglycemia (diabetes, impaired fasting glucose), and metabolic syndrome.
Age, BMI, waist circumference, triglycerides, and HDL-C
were treated as continuous variables. Separate analyses
were performed assessing the impact of metabolic syndrome as diagnosed by each definition in the risk of
uncontrolled hypertension. The number of metabolic risk
factors associated with uncontrolled hypertension and the
pattern of antihypertensive treatment were also considered
in alternative models. The goodness of fit of the models was
evaluated by the area under the receiver-operating characteristic (ROC) curve, sensitivity and specificity. All analyses
were two-sided and a P < 0.05 was considered statistically
significant. The statistical analysis was performed using the
SPSS 19.0 software package (SPSS Inc., Chicago, Illinois,
USA).
RESULTS
In total, 16 856 outpatients were recruited in the survey. Of
these, 8925 hypertensive patients under antihypertensive
treatment were included in the analyses. The mean age was
64.5 11.1 years and 60.1% were women. Obesity was
observed in 39.5% of patients and 66.7% had abdominal
obesity. The majority of hypertensive patients had metabolic syndrome. Considering the several definitions, NCEPATP 2001: 54.1%; NCEP-ATP 2004: 60.9%; IDF: 62.9%, and
AHA-NHLBI: 81.9%.
Regarding the antihypertensive treatment, monotherapy
was used in 47.6% of patients. The percentages for hypertensives treated with two, three and at least four drug
classes were 36.2, 13.0, and 3.2%, respectively. The most
commonly used drugs were diuretics, prescribed in 47.4%,
either alone or in association with other classes. Angiotensin receptor blockers were used in 43.0% and angiotensinconverting enzyme inhibitors were prescribed in 39.2% of
patients. Calcium channel blockers were prescribed in
18.9%, increasing with advancing age in both sexes. Finally,
b-blockers were used in 16.2%, being more frequently used
in young adults (18–39 years: 22.4%). Further details on the
patterns of antihypertensive treatment are presented in the
Supplemental Table 1, http://links.lww.com/HJH/A268.
Volume 31 Number 10 October 2013
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Metabolic syndrome and hypertension
TABLE 1. Adapted diagnostic criteria of metabolic syndrome
Clinical parameter
Waist circumference
Triglycerides
HDL-cholesterol
Glucose
NCEP-ATP III2001
NCEP-ATP III2004
AHA-NHLBI2005
Association of 2
of the following
Association of 2
of the following
IDF2005
M: 102 cm
W: 88 cm
150 mg/dl
M: 102 cm
W: 88 cm
150 mg/dl
M: 94 cm; W: 80 cm
Plus 1 of the following:
150 mg/dl
M: <40 mg/dl
W: <50mg/dl
Fasting glucose
>110 mg/dl
M: <40 mg/dl
W: <50mg/dl
Fasting glucose
>100 mg/dl
M: <40 mg/dl
W: <50mg/dl
Fasting glucose
>100 mg/dl or
antidiabetic therapy
Association of 2
of the following
M: 102 cm
W: 88 cm
150 mg/dl or lipid-modifying drug
therapy
M: <40 mg/dl; W: <50mg/dl
or lipid-modifying drug therapy
Fasting glucose >100 mg/dl or
antidiabetic therapy
AHA-NHLBI, American Heart Association/National Heart, Lung and Blood Institute; IDF, International Diabetes Federation; M, men; NCEP-ATP III, National Cholesterol Education Program
Adult Treatment Panel III; W, women.
45
40.0%
150
130
110
35
30
23.7%
25
18.9%
20
15
170
12.1%
10
5.3%
Diastolic BP (mmHg)
Prevalence (%)
40
We performed additional univariate analyses assessing
the influence of metabolic risk factors and metabolic syndrome in BP measurements (see Supplemental Tables 4 and
5, http://links.lww.com/HJH/A268, which demonstrate that
SBP and DBP tended to be higher in the presence of
metabolic risk factors).
The occurrence of uncontrolled hypertension was compared with the number of metabolic risk factors coaggregated in each patient, as defined by each definition of
metabolic syndrome (Table 3). The risk of uncontrolled
hypertension significantly increased with the number of
coaggregated metabolic risk factors. However, the magnitude of the effect significantly depended on the definition
applied. The increase of the risk of poor BP control was
more progressive when the NCEP-ATP III definitions (2001
and 2004) were applied and it doubled in patients with
coaggregation of all the four metabolic risk factors.
Finally, patients with metabolic syndrome were at significantly higher risk of resistant hypertension (defined as
BP that remains uncontrolled in spite of the concurrent use
of three different antihypertensive drug classes or the need
of more than three medications to get adequate BP control),
irrespective of the applied definition of metabolic syndrome. Considering the several definitions, NCEP-ATP
Systolic BP (mmHg)
BP was controlled in 35.8% of the treated hypertensive
patients. BP was 160–179/100–109 mmHg in 18.9% of
patients (men: 19.7%; women: 18.3%) and 180/110 mmHg
in 5.3% (men: 5.1%; women: 5.6%) – Fig. 1. SBP was higher in
men (144.7 18.3 vs.143.7 18.6 mmHg; P < 0.001). In contrast, DBP was similar in both sexes (81.6 11.0 mmHg). BP
progressively increased with age up to 50–59 years. From the
age of 60, SBP continued to rise but DBP progressively
decreased (Fig. 2).
Table 2 depicts the association of uncontrolled hypertension with the demographic characteristics and metabolic
risk factors as determined by logistic regression multivariate
analysis. All of the four metabolic risk factors composing the
metabolic syndrome definition were associated with
uncontrolled BP. In addition, metabolic syndrome was a
relevant independent risk factor for poor BP control: applying the NCEP-ATP III definitions, metabolic syndrome was
the strongest treatable risk predictor for uncontrolled hypertension (NCEP-ATP 2001: odds ratio 1.23; NCEP-ATP
2004: odds ratio 1.15) and that effect was independent of
the individual components of the syndrome. Of note,
metabolic syndrome ceased to be an independent predictor
of poor BP control when the IDF or AHA-NHLBI definitions
were applied, although the specific impact of the individual
components of the syndrome was maintained. This association was independent of the pattern of antihypertensive
treatment, either assessed by the drug class in use or by the
number of drug classes prescribed, as shown in the Supplemental Tables 2 and 3, http://links.lww.com/HJH/A268.
90
70
50
5
18–29
0
Optimal
Normal
Grade I
Grade II
Grade III
Systolic BP
<130
130–139
140–159
160–179
≥180
Diastolic BP
<80
80–89
90–99
100–109
≥110
FIGURE 1 Distribution of the patients on antihypertensive treatment by hypertension grade. Error bars are representing the 95% confidence interval.
Journal of Hypertension
30–39
40–49
50–59
60–69
70–79
≥80
Age groups
Overall population
Men
Women
FIGURE 2 Distribution of the SBP and DBP by age and sex. Error bars are representing mean SD. Overall population: squares; men: diamonds; women: circles.
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1994
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(1.43–2.00)
(1.69–2.75)
(1.84–3.28)
(1.94–3.64)
(2.02–3.90)
(0.00–10.40)
(1.005–1.017)
(1.001–1.002)
(1.002–1.009)
40.12%/71.59%
58.16%/54.04%
–
–
1.15 (1.01–1.30)
(1.43–2.00)
(1.69–2.76)
(1.84–3.28)
(1.95–3.64)
(2.01–3.90)
–
1.012 (1.006–1.017)
1.002 (1.001–1.003)
1.004 (1.001–1.008)
1.69
2.15
2.46
2.66
2.80
–
Odds ratio
(95% CI)
0.58 (0.57–0.60)
NS
NS
0.002
<0.001
<0.001
<0.001
<0.001
<0.001
NS
<0.001
<0.001
0.003
NS
P
0.59 (0.57–0.60)
1.15 (0.94–1.41)
0.91 (0.81–1.02)
1.23 (1.08–1.41)
1.69
2.15
2.46
2.66
2.80
0.00
1.011
1.002
1.005
–
Odds ratio
(95% CI)
NS
NS
0.037
<0.001
<0.001
<0.001
<0.001
<0.001
NS
<0.001
<0.001
0.009
NS
P
NCEP-ATP2004 MS definition
47.96%/64.76%
0.58 (0.57–0.60)
1.30 (1.06–1.61)
–
–
(1.41–2.04)
(1.66–2.84)
(1.81–3.39)
(1.91–3.78)
(1.97–4.06)
–
1.013 (1.007–1.019)
1.002 (1.001–1.003)
1.005 (1.001–1.008)
1.70
2.17
2.48
2.69
2.83
–
Odds ratio
(95% CI)
NS
P
0.013
NS
NS
<0.001
<0.001
<0.001
<0.001
<0.001
NS
<0.001
<0.001
0.009
IDF2005 MS definition
48.01%/64.10%
0.58 (0.57–0.59)
1.25 (1.02–1.53)
–
–
(1.35–1.92)
(1.55–2.56)
(1.68–3.06)
(1.76–3.38)
(1.81–3.61)
–
1.013 (1.007–1.018)
1.002 (1.001–1.003)
1.005 (1.001–1.008)
1.61
2.01
2.27
2.44
2.55
–
Odds ratio
(95% CI)
0.031
NS
NS
<0.001
<0.001
<0.001
<0.001
<0.001
NS
<0.001
<0.001
0.007
NS
P
AHA-NHLBI2005 MS definition
Separate multivariate logistic regression analyses were performed to compare the impact of metabolic syndrome as diagnosed by the several definitions. AHA-NHLBI, American Heart Association/National Heart, Lung and Blood Institute;
95% CI, 95% confidence interval; IDF, International Diabetes Federation; MS, metabolic syndrome; NCEP-ATP III, National Cholesterol Education Program Adult Treatment Panel III; NS, non-significant; ROC, receiver operator characteristic.
Statistical model: 7770 individuals included in the analysis; event rate: 66.6% (N ¼ 5172). Reference classes for categorical variables: female sex, normal glucose metabolism (fasting glucose <110 mg/dl, without antidiabetic therapy), without
metabolic syndrome. Age, BMI, waist circumference, triglycerides, and HDL-cholesterol were treated as continuous variables.
Male sex
Age – from 30 to:
40 years
50 years
60 years
70 years
80 years
BMI (kg/m2)
Waist circumference (cm)
Triglycerides (mg/dl)
HDL-cholesterol (mg/dl)
Glucose metabolism
Impaired fasting glucose
Diabetes mellitus
Metabolic syndrome
Accuracy of the model
Area under the ROC
curve (95% CI)
Sensitivity/Specificity
Variable
NCEP-ATP2001 MS definition
TABLE 2. Occurrence of uncontrolled HT in relation to demographic characteristics and metabolic risk factors
Cortez-Dias et al.
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Metabolic syndrome and hypertension
TABLE 3. Occurrence of uncontrolled HT in relation to demographic characteristics and the number of coaggregated metabolic risk factors
NCEP ATP2004
MS definition
NCEP ATP2001
MS definition
Variable
Odds ratio
(95% CI)
P
Male sex
1.17 (1.06–1.29)
0.001
Age (years)
30–39
–
NS
40–49
2.37 (1.04–5.38)
0.04
50–59
2.44 (1.09–5.50)
0.03
60–69
2.80 (1.25–6.29)
0.01
70–79
2.89 (1.29–6.49)
0.01
80
2.80 (1.23–6.38)
0.01
Number of metabolic risk factors
One
1.23 (1.06–1.42)
0.005
Two
1.49 (1.29–1.73)
<0.001
Three
1.93 (1.63–2.27)
<0.001
Four
1.99 (1.57–2.50)
<0.001
Accuracy of the model
Area under ROC
0.57 (0.56–0.58)
(95% CI)
Sensitivity/specificity
55.05%/54.58%
Odds ratio
(95% CI)
P
1.16 (1.05–1.28)
2.34
2.42
2.77
2.86
2.77
NS
0-04
0.03
0.01
0.01
0.02
NS
<0.001
<0.001
<0.001
AHA-NHLBI2005
MS definition
Odds ratio
(95% CI)
P
Odds ratio
(95% CI)
P
–
NS
–
NS
–
–
–
2.53 (1.06–6.06)
2.61 (1.09–6.26)
2.68 (1.10–6.54)
NS
NS
NS
0.04
0.03
0.03
–
–
–
2.50 (1.04–5.98)
2.60 (1.08–6.22)
2.66 (1.09–6.50)
NS
NS
NS
0.04
0.03
0.03
0.04
0.01
0.01
<0.001
–
1.41 (1.12–1.78)
1.27 (1.02–1.59)
1.73 (1.38–2.17)
NS
0.004
0.04
<0.001
0.002
–
(1.03–5.31)
(1.08–5.45)
(1.23–6.21)
(1.27–6.42)
(1.22–6.31)
–
1.38 (1.28–1.61)
1.71 (1.45–2.03)
1.96 (1.57–2.44)
IDF2005
MS definition
1.43
1.60
1.57
1.90
(1.01–2.01)
(1.15–2.24)
(1.13–2.17)
(1.37–2.63)
0.56 (0.55–0.58)
0.55 (0.53–0.56)
0.56 (0.54–0.57)
50.44%/59.08%
54.87%/51.68%
39.24%/68.97%
Separate multivariate logistic regression analyses were performed to compare the impact of metabolic syndrome as diagnosed by the several definitions. AHA-NHLBI, American Heart
Association/National Heart, Lung and Blood Institute; 95% CI, 95% confidence interval; IDF, International Diabetes Federation; MS, metabolic syndrome; NCEP-ATP III, National
Cholesterol Education Program Adult Treatment Panel III; NS, non significant. Statistical model: 8258 treated hypertensive patients included in the analysis; event rate: 66.7%
(N ¼ 5504); reference classes: female sex, age 18–29 years old, no metabolic risk factors.
2001: OR ¼ 1.58 (95% CI 1.39–1.79); NCEP-ATP 2004:
OR ¼ 1.56 (95% CI 1.36–1.78); IDF: OR ¼ 1.60 (95% CI
1.39–1.85) and AHA-NHLBI: OR ¼ 1.64 (95% CI 1.35–1.99).
DISCUSSION
This study shows that metabolic risk factors decrease the
probability of correct BP control in a population of pharmacologically treated hypertensive patients. In that sense,
metabolic syndrome appears as a loss of chance to be
appropriately managed by antihypertensive drugs. However, this effect significantly depends on the criteria used for
metabolic syndrome diagnosis, and the NCEP-ATP III
(2001) definition provided the highest incremental value
in the identification of hypertensive patients at risk of poor
BP control.
The BP control rate observed in this Portuguese population is worryingly low (35.4%), but similar results
have been observed in other European countries [3–6].
The identification of risk factors for poor BP control may be
of special importance, contributing to a better planning of
the preventive health strategies.
Hypertension and metabolic risk factors are relevant
public health problems. Surveys throughout the world have
revealed dramatic increases in the prevalence of obesity in
many countries. In the United States, almost one-third of the
adult population is obese [21]. In Portugal, the prevalence
of obesity is estimated to be 19.9% in adults aged 18–64
years [22]. Among older Portuguese individuals, obesity
affects 16.8% of men and 21.8% of women [22]. In addition,
there is a clear relationship between BMI and BP, being
obesity much more prevalent in hypertensive populations
throughout the world [23–26]. Risk estimates from the
Framingham Heart Study suggest that approximately 78%
of primary hypertension in men and 65% in women can be
Journal of Hypertension
ascribed to obesity [25]. In accordance, we observed high
prevalence of obesity (39.5%) and abdominal obesity
(66.7%) in our hypertensive patients. Previous reports have
also shown that approximately 38–62% of patients with
hypertension have at least two additional metabolic risk
factors [19,27,28] and it has been suggested that poor BP
control tends to be associated with obesity [13,15,16,19],
abdominal obesity [13,19], dyslipidemia [15,19], and glucose metabolism disturbances [15,19]. Arcucci et al. [14]
observed that BP control worsens with the number of
clustered metabolic risk factors and other studies reported
higher prevalence of uncontrolled hypertension in patients
with metabolic syndrome phenotype as diagnosed by the
NCEP-ATP III definition [14,19]. It should be noted that
none of these studies distinguished the impact of metabolic
syndrome from the effect of its individual components in
the BP control. Furthermore, the impact of metabolic syndrome as diagnosed by the several definitions was never
compared.
In this study, we confirmed the influence of metabolic
risk factors in the effectiveness of the antihypertensive
treatment. We also found strong evidence of the additional
risk determined by their coaggregation as metabolic syndrome, implying that the interaction of metabolic risk
factors influences the cardiovascular risk in a magnitude
that goes higher behind the individual components of the
metabolic syndrome definition. The pathophysiological
mechanisms involved in this interaction remain to be identified. Obesity, insulin resistance and adipocyte cytokines
have been linked to endothelial dysfunction and hypertension [29]. So, it is reasonable that metabolic syndrome may
be causative of poor BP control.
We also observed that the relation between metabolic
syndrome and BP control significantly depends on the
criteria used to define metabolic syndrome. Although the
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Cortez-Dias et al.
differences between the several definitions of metabolic
syndrome may appear slight, they are in fact significant.
According to the IDF definition, abdominal obesity is
essential and the waist circumference cut-offs are lower
and with ethnicity-specific values. In the IDF, AHA-NHLBI,
and NCEP-ATP III 2004 definitions, the fasting glucose cutoff was lowered from 110 to 100 mg/dl in order to bring the
definition of metabolic syndrome into line with the American Diabetes Association’s definition of prediabetes [30].
Finally, the need for antidiabetic therapy, lipid-lowering
drugs, or antihypertensive medication is considered to be
equivalent to the presence of the corresponding risk factor
in the IDF and AHA-NHLBI definitions. As a result of these
differences, the agreement between definitions is known to
be moderate: only 60.3% of patients with suspected metabolic syndrome met the diagnostic criteria of all the four
definitions [31]. In our study, metabolic syndrome was
identified as a strong independent predictor of poor BP
control, but that effect occurred only when the NCEP-ATP
III 2001 or 2004 definitions were applied. This result has
major clinical implications, as the only difference between
the NCEP-ATP III 2004 and the AHA-NHLBI definitions
results from considering the antidiabetic and lipid-lowering
treatments as risk factor equivalents in the latter. In that
sense, the loss of effect when applying the AHA-NHLBI
definition implies that patients treated with antidiabetic or
lipid-lowering drugs that have normal levels of fasting
glucose, triglycerides, and HDL-C are no longer at higher
risk of poor BP control. In this sense, the strict control of all
the metabolic risk factors in hypertensive patients appears
to increase the chance of good BP control. Of note, these
results were independent of the pattern of antihypertensive
drug treatment.
Limitations
This observational study used a nonrandom sample of
hypertensive patients and might be subjected to
unweighted selection bias. In order to offset this possibility,
patients were enrolled irrespectively of the presence of
cardiovascular risk factors. The Portuguese National Health
Service requires that hypertensive patients consult their
general practitioners regularly to renew their prescriptions.
Therefore, primary heath care users under antihypertensive
medication can be considered representative of the treated
hypertensive population. Potential confounding effects due
to overrepresentation of particular groups such as diabetics,
women, or older individuals were restricted by the logistic
analysis method.
The association between metabolic risk factors and poor
BP control was confirmed even when the pattern of antihypertensive treatment (drug class and number of antihypertensive drugs) was included in the logistic regression
model. However, we recognize that data not collected on
our study on the dosage and duration of treatment with
each antihypertensive drug, BP prior to current regimen,
quantification of the compliance to the treatment, number
of pills to be taken and data from hypertension diagnosis
would be also relevant. In fact, the demographic variables
and metabolic risk factors included in our model only
explained part of the risk of poor BP control (area under
the ROC curve of 0.58–0.59).
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In conclusion, hypertension is the leading risk factor for
cardiovascular morbidity and mortality. Despite the social
and financial effort devoted to reduce BP, only a small
proportion of hypertensive patients are optimally controlled. The identification of factors influencing the effectiveness of antihypertensive treatment is of great relevance.
Our data suggest that metabolic risk factors affect the
chance of achieving optimal BP control. However, more
than single metabolic risk factors, their coaggregation as
metabolic syndrome is associated with poor BP control. In
fact, metabolic syndrome as defined by NCEP-ATP III
diagnostic criteria was the strongest treatable risk factor
for uncontrolled hypertension. Instead, the IDF and AHANHLBI definitions of metabolic syndrome were not useful
to identify hypertensive patients at risk of poor BP control.
As metabolic risk factors strongly influence the success of
BP control more effort should be made to recognize and
aggressively treat both high BP and the concomitant metabolic risk factors.
ACKNOWLEDGEMENTS
The VALSIM Study received scientific support from the
Portuguese Society of Cardiology, the Directorate-General
of Health, and the Portuguese Association of General
Practice Physicians. For this study, the Portuguese Society
of Cardiology received an unrestricted grant from Novartis.
Conflicts of interest
The authors do not have conflicts of interest to declare.
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Reviewers’ Summary Evaluations
number of different medications as well as the number of
tablets to be taken by the patient and blood pressure
control. Therefore, hypertensive patients with the metabolic syndrome are likely to be on an increased number of
medications affecting the compliance to their treatment.
This likely represents an important factor which did not
form part of the present analyses.
Reviewer 1
Strength of the study: In this study, the influence of the
presence of the metabolic syndrome on blood pressure
control has been investigated in a relatively large community study. Therefore, the conclusions of the paper
allow some extrapolation to the hypertensive population
at large. The study shows that uncontrolled hypertension
is increased in the presence of a metabolic syndrome, but
the results differed depending on the definition of the
metabolic syndrome. Not unexpected but nicely documented is the observation that the occurrence of uncontrolled hypertension increased with the number of risk
factors within the metabolic syndrome. Another interesting observation is that the class of antihypertensive medications was not a determinant for lack of hypertension
control.
Weakness of the study: Although the presence of the
metabolic syndrome was closely associated with uncontrolled hypertension, it explained only part of their problem, as shown by the analysis of the area under the ROC
curve. There is a well known relationship between the
Journal of Hypertension
Reviewer 2
Cortez-Dias and coworkers demonstrated that metabolic
syndrome lowers the effectiveness of antihypertensive
treatment. The strength of this paper is to demonstrate a
relationship that had been suggested as an hypothesis so
far. However, the present work, as most papers investigating BP control, lacks data on treatment compliance. The
second weakness is the choice of a population sample in
which the prevalence of adiposity was particularly high. In
this kind of population the relationship between BP and
overweight is very close, but may not be so strict in other
samples. Despite these limitations these results add important information to the interpretation of resistance to
antihypertensive therapy.
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1997
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