Shimabukuro et al. Cardiovasc Diabetol (2016) 15:77
DOI 10.1186/s12933-016-0394-5

ORIGINAL INVESTIGATION

Cardiovascular Diabetology
Open Access

Impact of individual metabolic risk
components or its clustering on endothelialand smooth muscle cell function in men
Michio Shimabukuro1* , Namio Higa2, Hiroaki Masuzaki3, Masataka Sata4 and Shinichiro Ueda5

Abstract
Background: Impaired vasoreactivity is often observed in subjects with metabolic syndrome, a condition that
includes the presence of a specific cluster of risk factors for obesity and cardiovascular disease. However, hierarchical causes in the impaired vasoreactivity have not been clarified. We evaluated the impact of individual metabolicrisk components or its clustering under the condition of insulin resistance on endothelial and smooth muscle cellfunction.
Methods: Vascular reactivity to acetylcholine (Ach), with or without nitric oxide synthase (NOS) inhibitor NG-monomethyl-l-arginine (L-NMMA), or sodium nitroprusside (SNP) by forearm venous occlusion plethysmography andinsulin sensitivity index (M mg/kg/min) in euglycemic clamp were measured in men without (n = 18, control group)or with (n = 19, metabolic syndrome group) metabolic syndrome.
Results: (1) Ach-induced maximal forearm blood flow (maxFBF) was impaired in subjects with metabolic syndrome.
In particular, the NOS-dependent component of Ach-induced maxFBF was selectively decreased, while the NOSindependent component remained relatively unchanged. (2) Ach-induced maxFBF and ∆Ach-induced maxFBF with
L-NMMA were correlated with waist circumference, glucose, and triglycerides, and most strongly correlated with visceral fat area, adiponectin, and M. (3) Multivariate regression analysis indicated that individual metabolic risk components explained Ach-induced maxFBF by 4–21 %. Clustering of all metabolic risk components increased this to 35 %,and the presence of metabolic syndrome explained 30 %, indicating that defining metabolic syndrome can effectively
predict impairment of endothelial dysfunction.
Conclusions: Endothelial dysfunction was correlated with individual metabolic risk components, but more strongly
with clustering of the components under a condition with low insulin sensitivity. We suggest that in subjects withmetabolic syndrome, endothelial function is impaired by multiple cardiovascular risk factors exclusively when under
the condition of insulin insensitivity and also that defining metabolic syndrome can effectively predict impairment ofendothelial dysfunction.
Keywords: Obesity, Metabolic syndrome, Endothelial function, Insulin resistance
Background
Metabolic syndrome is a condition that includes the presence of a specific cluster of risk factors for obesity andcardiovascular disease, including abdominal obesity, high*Correspondence: mshimabukuro‑ur@umin.ac.jp
1
Department of Cardio‑Diabetes Medicine, Institute of Biomedical
Sciences, Tokushima University Graduate School, 3‑18‑15 Kuramoto,Tokushima 770‑8503, Japan
Full list of author information is available at the end of the article

blood pressure, impaired fasting blood glucose, hypertriglyceridemia and low HDL cholesterol [1]. Of note, theclinical utility of metabolic syndrome has been questioned [2, 3], because different definitions and different
clusterings of components of metabolic syndrome may
result in variations in cardiovascular risk predictions [4].
Impairment of vasoreactivity has been observed inpatients with traditional coronary risk factors, even in
the absence of morphological atherosclerotic lesions [5].

© 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/
publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Shimabukuro et al. Cardiovasc Diabetol (2016) 15:77

Accordingly, the assessment of vasoreactivity can provide
pivotal information as a diagnostic and prognostic tool inpatients at risk for atherosclerotic cardiovascular disease[6, 7]. The vasoreactivity is often impaired in subjects
with metabolic syndrome [8, 9]. Hence, the impaired vasoreactivity in this syndrome can be provoked by two scenarios [1]: (1) individual metabolic risk components suchas high blood pressure, hyperglycemia, and dyslipidemia,or (2) its clustering under the condition of obesity and/or insulin resistance. However, the principal scenario forthe vascular dysfunction in metabolic syndrome remains
obscure.
The current study compared the impact of individual metabolic risk components and its clustering under
the condition of obesity and/or insulin resistance on
endothelial and smooth muscle cell function in subjects
with metabolic syndrome.

Methods
Subjects

Male subjects were divided into either the group without metabolic syndrome (n = 18, control group) orwith metabolic syndrome (n = 19, MS group). A subject was defined as having metabolic syndrome as per
the guidelines outlined in the IDF consensus statement[10]. Therefore, the subject had metabolic syndrome if
he was obese (according to the Japanese criteria, having a waist circumference ≥85 cm in men) and had
any two of the following four factors: (1) hypertriglyceridemia [serum triglyceride concentration ≥150 mg/
dL (1.69 mmol/L)], (2) a low HDL cholesterol level[serum HDL cholesterol concentration of 40 mg/dL(1.04 mmol/L)], (3) an elevated blood pressure (systolic blood pressure ≥130 mmHg and/or diastolicblood pressure ≥85 mmHg) or was taking anti-hypertensive drugs, and (4) a high fasting plasma glucose
level [fasting plasma glucose concentration ≥100 mg/
dL (5.6 mmol/L)]. Participants who were taking insulin
regimen or oral anti-diabetic drugs were excluded. Waist
circumference was measured in the standing positionand subcutaneous fat area (SFA) and intra-abdominal
visceral fat area (VFA) were determined at the level ofthe umbilicus using a standardized method involving
computed tomography [11]. Subjects were instructed torefrain from vigorous exercise, anti-hypertensive drugs,
anti-hyperlipidemic drugs, non-steroidal anti-inflammatory drugs, alcohol, smoking and caffeine for 24 h prior
to the study day. The study protocol was approved by the
Ethical Committee of the University of the Ryukyus, andobeyed to the standards set by the Declaration of Helsinki. Written informed consent was obtained from all
subjects.

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Biochemical measurements

Venous blood samples were obtained in tubes containing EDTA-sodium (1 mg/mL) and in polystyrene tubes
without an anticoagulant. The EDTA-containing tubes
were promptly chilled. Plasma was immediately separated
by centrifugation at 3000 rpm and 4 °C for 10 min, andserum isolated by centrifugation at 1000 rpm at room
temperature for 10 min. Samples were stored at −80 °C
until they were assayed. Routine chemical methods wereused to determine the serum concentrations of total cholesterol, HDL cholesterol, triglycerides, creatinine, glucose, and electrolytes. The serum concentration of LDL
cholesterol was estimated using Friedewald’s method [12].
Euglycemic hyperinsulinemic clamp

The whole-body insulin sensitivity index (M) was measured
using a hyperinsulinemic euglycemic clamp [13] with modifications [14] for 180 min. A primed continuous infusionof insulin (10.8 pmol kg−1 min−1, Novo-Nordisk, Japan)
was administered along with a variable rate infusion of 20 %dextrose (Baxter Health Care, Japan) that was adjusted
manually to maintain serum glucose of 5.2 mmol/L. Thiswas determined based on arterialized samples withdrawn
every 5 min from an ipsilateral right dorsal hand vein(heated-air blanket was kept at 55 °C). The M value (mg/kg/min) was calculated during the last 30 min of the study.
Vascular reactivity

Forearm blood flow (FBF) was measured simultaneously inboth forearms by bilateral venous occlusion plethysmography with mercury-in-silicone elastomer strain gauges, as
described [14]. All subjects were supine in a quiet, air-conditioned room, with both forearms resting slightly above
their heart level. Acetylcholine (Ach, 0–400 nmol/min)with or without NG-monomethyl-l-arginine (L-NMMA,
8 µmol/min), a nitric oxide synthase (NOS) inhibitor, or
sodium nitroprusside (SNP, 0–30 nmol/min) was infused
into a 27-gauge catheter inserted into the brachial arteryof the non-dominant arm by volumetric precision pumps.
The infusion rate was maintained at 1 mL/min throughout the study unless otherwise indicated. All FBF data
were obtained via a Mac Lab Version 4 chart recorder (ADInstruments, Hamstead, London, United Kingdom).
Statistical analysis

Values are expressed as the mean ± SD. Comparisons ofvascular responses were analyzed by two-way analysis ofvariance (ANOVA) for repeated measures on one factor, followed by Holm–Sidak multiple comparison test
to compare group means. Simple regression analysis wasused to identify significant linear associations of vascularresponses with components of the metabolic syndrome

Shimabukuro et al. Cardiovasc Diabetol (2016) 15:77

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and related variables. Multiple regression analysis for
vascular reactivity was performed in standard sequential(hierarchical) models and in a stepwise backward modelusing variables of individual components in the definitionof metabolic syndrome and markers for possible underlying mechanisms. All analyses were performed using Jump
version 12.1.0 software (SAS Institute Inc., Cary, NC). A
P value of <0.05 was considered statistically significant.

Results
General characteristics

Subject characteristics are summarized in Table 1. All
subjects with or without metabolic syndrome completed
the study. Subjects in the MS group were clinically obese,and displayed a higher body weight, BMI, and waist circumference than subjects without metabolic syndrome.
The MS group also had greater SFA and VFA. There wereno significant differences in age, heart rate, or smoking
status (control 20 %, MS 16 %) between the two groups.
The MS group exhibited higher levels of fasting glucose,
insulin, HbA1c, total and LDL cholesterol, and triglycerides and lower levels of HDL cholesterol than the control
group. The MS group exhibited lower adiponectin thanthe control group (p < 0.0001). In addition, M values werereduced by half in the MS group.
Vascular reactivity
Vascular responsiveness to Ach

Basal FBF was 3.4 ± 1.3 mL/min/100 mL in the control
group and 3.2 ± 1.2 mL/min/100 mL in the MS group
Table 1 General characteristics of the studied patientsControl(n = 18)
Age (years)
Body weight (kg)Body mass index (kg/m2)
Waist circumference (cm)
Systolic blood pressure(mm Hg)Diastolic blood pressure(mm Hg)Heart rate (beats/min)
Glucose (mmol/L)
Insulin (pmol/L)
Haemoglobin A1c (%)
LDL cholesterol (mmol/L)
HDL cholesterol (mmol/L)
Triglyceride (mmol/L)
Adiponectin (log µg/ml)
M (mg/kg/min)

48 ± 1565.3 ± 8.4

Metabolicsyndrome (n = 19)
51 ± 976.4 ± 8.3**

23.1 ± 2.3

27.8 ± 2.7**

121 ± 13134 ± 19**84.1 ± 9.8
74 ± 768 ± 85.7 ± 1.6

93.9 ± 7.2**
81 ± 10*68 ± 117.5 ± 2.7*

44.5 ± 28.9

79.0 ± 42.0**

2.79 ± 0.70

3.13 ± 0.66

1.12 ± 0.70

2.53 ± 1.04**

5.4 ± 1.1

1.41 ± 0.48

0.87 ± 0.15

7.81 ± 2.86

Mean ± SD, * p < 0.05, ** p < 0.01 vs control

7.0 ± 2.1*

1.22 ± 0.20

0.72 ± 0.12**

4.14 ± 2.04**(p = 0.663). As shown in Fig. 1a, Ach-induced maximal
FBF (Ach-induced maxFBF) (•) was reduced in the MSgroup (14.0 ± 4.5 mL/min/100 mL) compared to the control group (24.2 ± 8.0 mL/min/100 mL, p < 0.0001).
NOS‑dependent vasodilation

Ach-induced maximal FBF during co-infusion of
L-NMMA was reduced to 14.3 ± 5.2 mL/min/100 mL in
the control group, but was not reduced in the MS group(13.0 ± 4.8 mL/min/100 mL) (Fig. 1a). The decline in
Ach-induced maximal FBF by L-NMMA (∆maxFBF by
L-NMMA), which represents the NOS-dependent vasodilation, was 10.2 ± 6.7 and 2.1 ± 2.4 mL/min/100 mL,
respectively (p < 0.0001).
Smooth muscle responsiveness to SNP

As shown in Fig. 1b, SNP-induced maximal FBF (SNPinduced maxFBF) was also reduced in the MS groupcompared to control group: the SNP-induced maxFBF
was 19.5 ± 8.0 mL/min/100 mL in the control groupand 14.4 ± 5.0 mL/min/100 mL in the MS group(p = 0.021).
Vascular reactivity and components of metabolic
syndrome

To explore the contributions of individual metabolic risk
components to altered vascular control, the relationships
between the components and endothelial and vascularsmooth muscle responses were assessed. Significant relationships were observed between Ach-induced maxFBF
and waist circumference, glucose, and triglycerides, suchthat higher levels of these components were associatedwith lower vasodilation (Fig. 2). SNP-induced maxFBF
was correlated only with glucose. In addition, ∆maxFBF
by L-NMMA was correlated with SBP, glucose andtriglycerides.
Vascular reactivity, abdominal fat distribution,
adiponectin, and insulin sensitivity

Next, we evaluated the impact of VFA, SFA, adiponectin, and M value, the markers for possible underlying mechanisms, on Ach-induced maxFBF (Fig. 3). A
strong negative relationship was observed between Achinduced maxFBF and VFA, but not SFA. Ach-induced
maxFBF was positively correlated with adiponectin, andmost strongly correlated with M value. SNP-induced
maximal FBF was not correlated with VFA, SFA, or adiponectin, but was positively correlated with M value. The∆maxFBF by L-NMMA was correlated with SBP, glucose
and triglycerides. In addition to Ach-induced maxFBF,∆maxFBF by L-NMMA was negatively correlated with
VFA, and positively correlated with adiponectin and M
value.

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Fig. 1 Forearm vascular reactivity to a ACh, with or without the nitric oxide synthase (NOS) inhibitor L-NMMA (8 μmol/min), or b SNP, as measured
by bilateral venous occlusion plethysmography in subjects without (n = 18) or with (n = 19) metabolic syndrome. FBF was measured simultaneously in subjects infused with Ach (closed circles), SNP (closed circles) or Ach plus L-NMMA (gray circles) and non-infused arms (open circles) using
bilateral venous occlusion plethysmography. FBF forearm blood flow, ACh acetylcholine, L-NMMA NG-monomethyl-l-arginine, SNP sodium nitroprusside. Data are expressed as mean ± SD

Multivariate regression analysis of vascular reactivity
determinants

Next, we determined the impact of individual metabolic risk components or the clustering of components
on vascular reactivity by multivariate regression models(Table 2).
For Ach-induced maxFBF, waist circumference(model 1) and triglycerides, but not blood pressure, glucose or HDL cholesterol (data not shown), were determinants of vascular reactivity, even after correcting forage and smoking status. Clustering of 5 components
increased the corrected R2 (model 2), and additionof M value to the clustering of 5 components further
increased the corrected R2, which reached 0.514 (model3). Meanwhile, the presence of metabolic syndrome,
though slightly decreased compared to the clustering of
5 components, was also a determinant of Ach-induced
maxFBF (model 4); addition of M value to the presence of metabolic syndrome increased the corrected R2(model 5).
For SNP FBF, waist circumference (model 1), bloodpressure, plasma glucose, triglycerides, and HDL cholesterol (data not shown), were not determinants of vascular
reactivity. Clustering of these components with or without M value (models 2 and 3), and the presence of metabolic syndrome with or without M value (models 4 and 5)
were also not determinants of vascular reactivity.
For ∆maxFBF by L-NMMA, waist circumference(model 1) and triglycerides, but not blood pressure, glucose or HDL cholesterol (data not shown), were determinants of vascular reactivity. Clustering of 5 components
and the presence of metabolic syndrome increased thecorrected R2 value (models 2 and 3), and addition of M

value further increased the corrected R2 value (models 4
and 5).
In the stepwise backward model (model 6) including
the above individual components, defining metabolic
syndrome and M value were significant predictors of
Ach-induced maxFBF, M value for SNP-induced maxFBF,
and defining metabolic syndrome for ∆maxFBF by
L-NMMA.
Vascular reactivity and the number of componentsof metabolic syndrome

Finally, we evaluated the relationship between vascular reactivity and the number of metabolic risk components in subjects with or without visceral obesity (Fig. 4).
In subjects without visceral obesity (waist circumference <85 cm), subjects with 1 or ≥2 of 4 components,
which included a high level of fasting glucose, elevatedblood pressure, hypertriglyceridemia, and a low level of
HDL cholesterol, did not show a statistical difference in
Ach-induced maxFBF, SNP and ∆maxFBF by L-NMMA,
compared to the group with 0 component. There wereno significant differences in VFA, adiponectin and M
value among the subgroups with 0, 1 and ≥2 components. In subjects with visceral obesity (waist circumference ≥85 cm), Ach-induced maxFBF was not decreasedin the group with 1 component compared to the groupwith 0 component, but was decreased in the groupwith ≥2 components. SNP was not different among thesubgroups with 0, 1 and ≥2 components. The ∆maxFBF
by L-NMMA was decreased in accordance with the number of the components. In the group with ≥3 components, there were no significant differences in VFA andadiponectin, but M value was significantly decreased.

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Fig. 2 Simple regression analysis between vascular reactivity and components of metabolic syndrome in men without (n = 18, open circles) orwith (n = 19, gray circles) metabolic syndrome. FBF at an Ach infusion of 400 nmol/min, FBF at an SNP infusion of 30 nmol/min, and ∆Ach-induced
maximal FBF by co-infusion of L-NMMA at 8 μmol/min. Linear regression analysis was done, and r and p values are shown

Discussion
The new findings of this study are: (1) Ach-induced
and SNP-induced maximal FBFs were impaired in subjects with metabolic syndrome. In particular, we firstfound that the NOS-dependent component of Achinduced maxFBF was selectively decreased, while the
NOS-independent component remained unchanged.
(2) Ach-induced maximal FBF and ∆Ach-induced
maximal FBF by L-NMMA, and SNP-induced maximal FBF were correlated strongly with M value thanwith individual metabolic components. (3) Multivariate regression models clearly indicated that defining
metabolic syndrome, as compared to individual metabolic components, predicts impairment of endothelial
dysfunction.
Vascular reactivity

As observed in previous studies of obese subjects [15,16], Ach-induced maxFBF, a marker of endothelial function, was reduced in MS group. We further investigatedvascular NO bioavailability by comparing the dose–
response curves of Ach with and without pharmacological NOS inhibition. In the NO clamp technique, theNOS-dependent and NOS-independent components

of FBF can be accurately determined [17]. Co-infusionof L-NMMA and Ach caused a decrease in the level ofinhibition of Ach-induced maxFBF in subjects withmetabolic syndrome, and equalized the Ach-induced
maximum FBF during NOS inhibition between the twogroups. This implies that the NOS-dependent component of Ach-induced maxFBF was selectively decreasedin subjects with metabolic syndrome, while the NOSindependent component remained relatively unchanged.
Classically, it has been believed that endothelial function is impaired, even in the first step of atherosclerosis,
but smooth muscle cell function is preserved even in theadvanced stages of atherosclerosis [5]. Conversely, current study showed that SNP-induced maximal FBF, amarker of smooth muscle responsiveness, was reducedin the MS group. The data obtained during NO inhibition by L-NMMA, suggest that the whole difference in
Ach reactivity between two groups can be mainly due
to a defect in the NOS-dependent NO synthesis. However, the impaired smooth muscle vasorelaxation suggests an impaired bioavailability and/or responsivenessto endogenous NO [18]. Given that the response to Ach
was superimposable at the levels in NO synthesis and/or its biological activities, the contradictory findings may

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Fig. 3 Simple regression analysis between vascular reactivity, abdominal fat distribution, adiponectin and insulin sensitivity index (M) in men
without (n = 18, open circles) or with (n = 19, gray circles) metabolic syndrome. FBF at an Ach infusion of 400 nmol/min, FBF at an SNP infusion of
30 nmol/min, and ∆Ach-induced maximal FBF by co-infusion of L-NMMA at 8 μmol/min. Pearson’s correlation coefficients and p values are shown

be partially explained by a converse compensation forthe loss in NO bioavailability [18]. Endothelium-derived
hyperpolarizing factor (EDHF) pathway, in which alterations was reported under pathological conditions, might
be one such candidate [19]. Fernandes et al. reported
that time-to-peak after hyperemia rather than flow
mediated dilation (FMD) distinguished metabolic syndrome from healthy controls [20]. In the time-course
analysis of FMD [21], time to peak after hyperemia is
not influenced by L-NMMA inhibition, suggesting that
other factors, such as differences in vascular complianceand transduction independently of the NOS pathway[20, 22].
Vascular reactivity, abdominal fat distribution, adiponectin
and insulin sensitivity

In the current study, Ach-induced maximal FBF,and ∆Ach-induced maximal FBF by L-NMMA werecorrelated with waist circumference, glucose and triglycerides, and more strongly with M value. This result
agrees with a previous study [15] where Steinberg
et al. report that obesity/insulin resistance is associated with both blunted endothelium-dependent vasodilation and failure of euglycemic hyperinsulinemia toaugment endothelium-dependent vasodilation. Thissuggests that obese/insulin-resistant subjects are characterized by endothelial dysfunction and endothelial resistance to insulin’s effect on enhancement ofendothelium-dependent vasodilation. Our results further indicated that each of abdominal fat distribution,
adiponectin, and insulin sensitivity was correlated with
Ach-induced maximal FBF and ∆Ach-induced maximal FBF by L-NMMA more greatly than each of the
MS components. Therefore, endothelial function inmetabolic syndrome can be explained mostly by mutually dependent insulin resistance, visceral obesity andShimabukuro et al. Cardiovasc Diabetol (2016) 15:77

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Table 2 Multivariate regression analysis fo vascular reactivity
Model 1aModel 2aModel 3aModel 4aModel 5aModel 6bCorrected R2

0.228

0.347

0.514

0.297

0.429

0.522

P value

0.009

0.006

0.001

0.002

0.000

VariablesE

Age (years)
Current smoking (yes or no)−0.153 0.156 −0.018 0.868 0.085

1.246

0.336 0.532

0.427 −0.109 0.288 0.016

Waist circumference (cm) ≥85 cm (yes or no)4.749

0.002 3.387

0.024 2.137

0.116

1.931

0.146 2.613

0.046

Ach-induced maximal forearm blood flow (FBF)P

SBP ≥130 or DBP ≥85 mmHg or use of antihypertensive drugs
Glucose ≥100 mg/dL (yes or no)

E

1.947

Triglycerides ≥150 mg/dL (yes or no)

HDL-cholesterol ≥150 mg/dL (yes or no)

P

E

P

E

0.665 −0.770 0.517 0.188

P

E

0.000
P

0.883 00.876 −0.991 0.419 00.131 −0.374 0.771

2.230

0.095 1.305

0.298

1.065

0.456 1.493

0.286

Metabolic syndrome (yes or no)4.675

M (mg/kg/min)

1.424

0.005

E

P
0.712
0.562

0

0.134

0

0.326

0

0.184

0

0.490

0

0.965

0.000 −3.120 0.022 −3.560 0.006
1.258

0.012 1.186

0.254

0.005

SNP-induced maximal forearm blood flow (FBF)
Corrected R2

0.123

0.117

0.148

0.173

0.224

P value

0.063

0.159

0.152

0.026

0.022

VariablesE

Age (years)

P

E

P

E

P

E

P

E

0.0017P−0.233 0.020 −0.153 0.168 −0.114 0.346 −0.213 0.029 −0.151 0.162 01.239

0.293 0.875

Waist circumference (cm) ≥85 cm (yes or no)1.669

0.202 1.477

0.304 0.507

0.735

0

0.818

0.437

0.736 1.123

0.432

0

0.415

2.355

0.069 0.649

0.655

0

0.438

−0.091 0.944 −0.810 0.563

0

0.726

SBP ≥130 or DBP ≥85 mmHg or use of antihypertensive drugs
Glucose ≥100 mg/dL (yes or no)

Triglycerides ≥150 mg/dL (yes or no)

HDL-cholesterol ≥150 mg/dL (yes or no)0.792

0.474 0.229

0.576 1.492

0.864 0.848

0.872

0.848 00.343

Metabolic syndrome (yes or no)
M (mg/kg/min)

0.448 0.232

0.240

Current smoking (yes or no)

0.107

0.906

0

0.519

−2.120 0.061 −1.022 0.430 0

0.336

0.784

0.103 1.237
0.481

0.002

∆Ach-induced maximal forearm blood flow (FBF) by L-NMMA
Corrected R2

0.236

0.460

0.594

0.406

0.533

P value

0.009

0.001

0.000

0.000

<0.0001

VariablesE

Age (years)−0.179 0.029 −0.071 0.344 −0.013 0.866 −0.145 0.045 0.074

Current smoking (yes or no)0.635

Waist circumference (cm) ≥85 cm (yes or no)3.048

SBP ≥130 or DBP ≥85 mmHg or use of antihypertensive drugs
Glucose ≥100 mg/dL (yes or no)

Triglycerides ≥150 mg/dL (yes or no)

HDL-cholesterol ≥150 mg/dL (yes or no)

P

E

P

E

P

b

Model 6: stepwise multiple regression analysis

E

0.512 −0.007 0.993 −0.726 0.397 −0.069 0.935 0.854<0.0001PE

0.415 0P
0.094

0.407 00.558

0.068 1.112

0.265

0

0.918

1.874

0.049 2.161

0.028

0

0.216

1.541

0.085 0.141

0.877

0

0.594

1.731

0.073 1.403

0.131

0

0.360

1.673

0.100 1.856

0.075

0

0.774

0.867

0.015

Metabolic syndrome (yes or no)

Model 1–5: standard multiple regression analysis

P

0.008 1.975

M (mg/kg/min)aEhypoadiponectinemia through increased production ofreactive oxidative species (ROS) and proinflammatory
cytokines [11, 23–29].
Impairment in the SNP-induced maximal FBF was
also correlated with M value as well as in Ach-induced−3.627 0.000 0.950
0.341

0.008 −15.01 0.000

0.021 00.112

maximal FBF. The observation may be supported by
Schinzari et al. showing that the vascular responsivenessto both Ach and SNP was not enhanced during hyperinsulinemia in patients with metabolic syndrome [30]. Theysuggests that insulin’s facilitator action on the vasodilator

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Fig. 4 The relationship between vascular reactivity and number of components of metabolic syndrome in men without visceral obesity (n = 12,
waist circumference <85 cm) or with visceral obesity (n = 25, waist circumference ≥85 cm). Subjects were divided into groups with 0, 1 or 2 ≥ofthe following metabolic syndrome components: high level of fasting glucose, elevated blood pressure, hypertriglyceridemia, and low level of HDLcholesterol. Data are expressed as mean ± SD. Statistical analysis was done by two-way analysis of variance (ANOVA), followed by Holm–Sidak
multiple comparison test to compare group means. P values = * <0.05, *** <0.001 vs. the 0 group

machinery was caused by endothelium unresponsiveness(a decrease in NO synthesis and/or a loss of its biological activities) and by defective sensitization of smoothmuscle vasorelaxation (a loss in NO bioavailability) [19].
Aoqui et al. found distinct patterns of microvascular dysfunction in metabolic syndrome, with augmented vasoconstriction present in the initial phase of metabolic
syndrome independent of endothelial dysfunction [31].
Reportedly, locally produced ROS [25] and/or fat-derived
ROS [23] can react with NO, generate peroxynitrite, andimpair cyclic GMP-dependent vasodilatation; this mechanism may partially explain these smooth muscle cell
dysfunction [18, 31].
Impact of individual metabolic risk components and its
clustering on endothelial and smooth muscle cell function

Since metabolic syndrome is a cluster of relatively heterogeneous atherosclerotic risk factors, which may solely
impair endothelial function, we evaluated the relationship between vascular reactivity and individual components or its clustering. In a simple regression model(Fig. 2), the Ach-induced maxFBF and ∆maxFBF by
L-NMMA were lineally correlated with waist circumference, glucose and triglycerides. However, multivariate
regression analysis indicated that individual components

explained only partially Ach-induced maxFBF by 4–21 %,∆maxFBF by L-NMMA by 6–31 %, and SNP-induced
maxFBF by 8–17 % (data not shown). In model 3, including the 5 components of visceral obesity, high fasting glucose, elevated blood pressure, hypertriglyceridemia, anda low level of HDL cholesterol, increased the assumption
to 35 % in Ach-induced maxFBF and 46 % in ∆maxFBF by
L-NMMA, but only 16 % in SNP-induced maxFBF. These
results support the notion that clustering of the components more greatly estimates endothelial dysfunction
than individual components, but not smooth muscle cellfunction. Notably, the presence of metabolic syndrome,
while still slightly less than the clustering of 5 components (model 2), explained 30 % of Ach-induced maxFBF
and 41 % of ∆maxFBF by L-NMMA (model 4). Thesefindings support the notion that defining metabolic syndrome is effective in predicting endothelial dysfunction,
which can subsequently predict future cardiovascular
events [5, 7]. In the current study, type 2 diabetes mellitus was prevalent in the MS group (12/18, 67 %) thanin the control group (4/19, 21 %) (p < 0.05). When all
patients divided into diabetic and non-diabetic groups,
Ach-induced maxFBF (21.5 ± 1.7 vs 16.0 ± 2.0 mL/
min/100 mL, p = 0.040), ∆maxFBF by L-NMMA(8.3 ± 6.8 vs 2.8 ± 4.2 mL/min/100 mL, p = 0.008) andShimabukuro et al. Cardiovasc Diabetol (2016) 15:77

SNP-induced maxFBF (19.4 ± 1.4 vs 13.6 ± 1.7 mL/
min/100 mL, p = 0.011) were all impaired in diabetic
groups. Vascular function in type 2 diabetes mellitus isalso confounded by cardiovascular risk factors, such as
obesity, hypertension and dyslipidemia, suggesting significance of cardiovascular risk clustering [32].
In our study, endothelial function is not strongly correlated with individual metabolic risk components,
but strongly with insulin resistance. Thus, it is assumed
that recovery of vascular function can be obtained lesseffectively by improvement of individual metabolic
parameters [33], but more effectively by improvementof metabolic syndrome or insulin resistance [34]. This
notion may be supported by the fact in patients withmetabolically healthy obesity (MHO), a medical condition characterized by obesity which does not produce
metabolic complications such as dyslipidemia, impaired
glucose tolerance or hypertension [35]. The MHO had
abnormal vascular reactivity, although their endothelial
dysfunction was less pronounced than in patients withmetabolic syndrome, indicating that obesity is associatedwith vascular damage independent of those metabolic
abnormalities underlying metabolic syndrome [36].
We also evaluated the relationship between vascular
reactivity and the number of metabolic syndrome components (Fig. 4). In subjects without visceral obesity,
there were no differences in Ach-induced maxFBF and∆maxFBF by L-NMMA among patients with 0, 1 and
2≥ of the 4 selected components. In contrast, in subjects with visceral obesity, Ach-induced maxFBF and∆maxFBF by L-NMMA decreased in subjects with ≥2components. As such, one or two components cannot
be sufficient to cause impairment of endothelial function
even in subjects with visceral obesity, but the clusteringof ≥2 components can be sufficient. Interestingly, subjects without visceral obesity that have ≥2 components
showed a subtler endothelial dysfunction than subjects
with visceral obesity. This notion agrees with the previous
study of Li et al., where they showed impaired endothelial
function in subjects with metabolic syndrome as compared to individuals with a similar burden of traditional
cardiovascular risk factors, but without metabolic syndrome [37]. Their multivariate regression model foundthat after adjustment for covariates and 6 traditional cardiovascular risk factors, the presence of metabolic syndrome had a significant and independent influence on
endothelial function (p < 0.01).
In our multivariate model, addition of M value
increased the corrected R2 for Ach-induced maxFBF to
51 % in the model including all the components (model3) and to 43 % in the model including the presence of
metabolic syndrome (model 5). Insulin resistance shouldplay a pivotal role in causing endothelial dysfunction by

Page 9 of 11

comorbidity of the metabolic syndrome components.
We also found that endothelial function, indicated by
Ach-induced maxFBF and ∆maxFBF by L-NMMA, wasimpaired in the group with ≥2 components, and M value
decreased according to the number of components. Collectively, our data suggest that in subjects with visceral fat
obesity, endothelial function is impaired by multiple cardiovascular risk factors exclusively when under the condition of insulin insensitivity.
Study limitationsFirst, we obtained data from a small number of subjects; therefore, there was the risk of type II errors.
The strain gauge plethysmography and the euglycemic
hyperinsulinemic clamp techniques, used in the study
for measurement of vascular function and insulin sensitivity, are sensitive and solid, but the time-consuming
and invasive characteristics limits number of participants. In contrast, alternative simplified methods suchas flow-mediated dilation (FMD) [38] and HOMA-IR[39] are good for recruiting participants, but the reliability is limited. Thus, current results should be confirmed by combinations of multiple techniques andclinical studies with different size. Second, this crosssectional study has shown only a correlation between
vascular dysfunction and insulin resistance in subjects
with metabolic syndrome, and has not indicated acause-effect relationship. In future studies, we need toconfirm that a therapeutic approach to improve insulin
sensitivity by such as reductions in visceral fat obesityand ectopic fat deposition can recover vascular dysfunction in metabolic syndrome. Third, since metabolic
syndrome is a cluster of relatively heterogeneous atherosclerotic risk factors, which solely affect endothelial function, careful consideration should be taken toevaluate underlying mechanisms. Forth, we could not
determine the molecular mechanisms by which insulin resistance occurs and impairs endothelial function
from the current study.

Conclusions
The current study evaluated the impact of individual
metabolic risk components or its clustering on endothelial and smooth muscle cell function in subjects withmetabolic syndrome. The endothelial and smooth muscle
cell function were correlated more strongly with clustering of the components under a condition with low insulin
sensitivity. Therefore, it may be suggested that in subjects
with metabolic syndrome, vascular reactivity is impairedby multiple cardiovascular risk factors exclusively under
the condition of insulin insensitivity and also that defining metabolic syndrome can effectively predict impairment of vascular reactivity.

Shimabukuro et al. Cardiovasc Diabetol (2016) 15:77

Authors’ contributions
MiS researched data and wrote the manuscript. NH researched data andreviewed the manuscript. HM, MaS and SU discussed data and reviewed themanuscript. All authors read and approved the final manuscript.
Author details
1
Department of Cardio‑Diabetes Medicine, Institute of Biomedical Sciences, Tokushima University Graduate School, 3‑18‑15 Kuramoto, Tokushima 770‑8503, Japan. 2 Cardiovascular Department, Naha City Hospital,
Okinawa, Japan. 3 Division of Endocrinology, Diabetes and Metabolism, Hematology, Rheumatology (Second Department of Internal Medicine), Graduate
School of Medicine, University of the Ryukyus, Okinawa, Japan. 4 Departmentof Cardiovascular Medicine, Institute of Biomedical Sciences, Tokushima
University Graduate School, Tokushima, Japan. 5 Department of Clinical Pharmacology and Therapeutics, Graduate School of Medicine, University of the
Ryukyus, Okinawa, Japan.
Acknowledgements
This study was supported by Grants from the Ministry of Education, Culture,
Sports, Science and Technology, Japan, #23591314, #24591338, #24591063.
Competing interests
The authors declare that they have no competing interests.
Received: 20 March 2016 Accepted: 7 May 2016

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