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Neck circumference is associated with non-traditional cardiovascular risk factors in individuals at low-to-moderate cardiovascular risk: cross-sectional analysis of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

  • 1,
  • 2,
  • 3, 4,
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  • 3,
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  • 6,
  • 6,
  • 6, 7,
  • 8,
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  • 2, 4Email author and
Diabetology & Metabolic Syndrome201810:82

https://doi.org/10.1186/s13098-018-0388-4

  • Received: 2 August 2018
  • Accepted: 14 November 2018
  • Published:

Abstract

Background

Neck circumference (NC) is associated with traditional cardiovascular risk factors (CVRF), but its usefulness to identify earlier atherogenic risk has been scarcely examined. Associations of NC with non-traditional CVRF were investigated in participants at low-to-moderate risk from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Methods

807 individuals (35–54 years) without obesity, diabetes or cardiovascular disease was stratified into quartiles of NC (cut-off for men: 36.5; 37.9 and 39.5 cm; women: 31.4; 32.5 and 34 cm) and traditional and non-traditional risk factors (lipoprotein subfractions by Vertical Auto Profile, adiponectin, leptin, E-selectin) were compared across groups. In linear regression models, associations of NC with non-traditional risk factors were tested for the entire sample and for low-risk group (≤ 2 CVRF).

Results

In both sexes, BMI, waist circumference, systolic and diastolic blood pressure, fasting and 2-h plasma glucose, HOMA-IR, triglycerides, leptin, E-selectin, small dense LDL-cholesterol, IDL-cholesterol, VLDL3-cholesterol and TG/HDL ratio increased significantly, while HDL2-cholesterol and HDL3-cholesterol decreased across NC quartiles. In linear regression models, a direct association [β(95% CI)] of NC with leptin [(0.155 (0.068–0.242); 0.147 (0.075–0.220)], E-selectin [(0.105 (0.032–0.177); 0.073 (0.006 to 0.140)] and small-dense LDL [(1.866 (0.641–3.091); 2.372 (1.391–3.353)] and an inverse association with HDL2-cholesterol [(− 0.519 (− 0.773 to − 0.266); − 0.815 (− 1.115 to 0.515)] adjusted for age were detected for men and women, respectively.

Conclusion

Our findings indicate that measurement of NC may be useful for an earlier identification of unfavorable atherogenic metabolic profile in middle-aged individuals at lower cardiovascular risk level.

Keywords

  • Neck circumference
  • Cardiovascular risk factors
  • Non-traditional risk factors
  • Adipocytokines
  • E-Selectin
  • Lipoprotein subfractions

Introduction

Cardiovascular disease (CVD) is the leading cause of disability adjusted life years (DALYs) worldwide, particularly in developing countries [1]. Earlier identification of at-risk individuals using novel risk markers could anticipate the implementation of preventive strategies.

Considering the role of insulin resistant adipose tissue in atherogenesis, measuring accurate adiposity indicators of cardiometabolic risk are clinically informative. Beyond the usefulness of body mass index (BMI) and waist circumference (WC), there is some evidence that neck circumference (NC) could also reflect upper-body fat deposition, enhancing the identification of high-risk individuals [2]. Increased NC has been reported in association with sleep apnea, elevated blood pressure, insulin resistance, lipid abnormalities and metabolic syndrome [3]. More recently, in studies including high-risk or diabetic individuals, NC was also associated with C-reactive protein (CRP), uric acid and carotid intimal-media thickness [4, 5]. How NC could help predicting cardiometabolic risk earlier has not been adequately investigated in large studies. The relationship between NC and non-traditional cardiovascular biomarkers in non-obese individuals without overt CVD warrants further investigation.

Great debate exists regarding the utility of circulating biomarkers of inflammation and endothelial dysfunction [6] and of atherogenic lipoprotein subfractions, which are increased in obesity prior to the development of overt type 2 diabetes or cardiovascular events [7]. Since these biomarkers play pathophysiological roles, they may represent an opportunity to identify risk earlier in the natural course of cardiometabolic diseases. E-selectin concentrations are associated with increased risk of diabetes mellitus [8] and calcium deposition in coronary arteries of low-to-moderate risk individuals [9]. Leptin and adiponectin are cytokines related to body adiposity and systemic inflammatory tone [10, 11]. Lipoprotein subfractions influence cardiovascular risk [12]. Increased serum concentrations of very low-density lipoproteins, remnant lipoproteins, small dense low-density lipoprotein (LDL), and decreased high-density lipoprotein (HDL) particles levels have been consistently associated with coronary heart disease [13, 17]. However, their predictive value for clinical practice is still not widely endorsed.

In this cross-sectional analysis of participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) [14], we hypothesized that NC could identify an atherogenic profile based on determinations of non-traditional cardiovascular risk factors, namely adipocytokines (adiponectin and leptin), an endothelial adhesion molecule (E-selectin), and lipoprotein sub fractions in non-obese individuals at low-to-moderate cardiovascular risk.

Methods

Design and population study

ELSA-Brasil [15] is an ongoing prospective cohort that enrolled 15,105 civil servants aged 35–74 years (54% women) in Brazil and aims to investigate type 2 diabetes, CVD and their risk factors [16]. The present cross-sectional analysis was based on the baseline data (carried out from August 2008 through December 2010) from a random sample of 1000 out of 5061 participants of the São Paulo research center included in a sub study aimed to evaluate the cardiometabolic profile based on non-traditional cardiovascular biomarkers (inflammatory and endothelial dysfunction biomarkers). The inclusion criteria were age range of 35–54 years and absence of diabetes (self-reported diabetes plus use of hypoglycemic drug or diabetic diagnosis by oral glucose tolerance test) and self-reported CVD. For the current analysis, obese individuals were excluded (BMI > 30 kg/m2). The sample was then composed of 807 participants. The institutional ethics committee approved the study and written consent was obtained from all participants.

Traditional CVRF

Body weight and height were measured using calibrated electronic scales and a fixed rigid stadiometer, while individuals wore light clothing without shoes. BMI was calculated as weight (in kilograms) divided by squared height (in meters). Waist circumference was measured with an inextensible tape according to the World Health Organization technique. NC was measured with individuals sitting and looking horizontally, using an inelastic tape, perpendicular to the long axis of the neck, right under the thyroid cartilage. Blood pressure was taken three times after a 5-min rest in the sitting position and the mean between the second and third measurements was used [16].

Participants underwent a 2-h 75-g oral glucose tolerance test (OGTT) for diagnosing categories of glucose tolerance [16]. Insulin resistance was estimated using the HOMA-IR index: fasting insulin (µUI/mL) × fasting glucose (mmol/L)/22.5 [17]. Plasma glucose was determined by the hexokinase method (ADVIA Chemistry; Siemens, Deerfield, Illinois, USA). ELISA kits were used for the determination of insulin (Siemens, Tarrytown, USA). Glomerular filtration rate (GFR) was estimated using the formula of Chronic Kidney Disease Epidemiology Collaboration—CKD-EPI [18].

Participants were categorized according to the presence of the following cardiovascular risk factors: (1) WC ≥ 102 cm for men or ≥ 88 cm for women; (2) systolic or diastolic blood pressure ≥ 130/85 mmHg or antihypertensive treatment; (3) fasting plasma glucose ≥ 100 mg/dL and < 126 mg/dL in the absence of antidiabetic agents; (4) triglyceride ≥ 150 mg/dL, or specific treatment; (5) HDL-C < 40 mg/dL for men and < 50 mg/dL for women, or specific treatment [20]. Those who had up to 2 abnormalities were considered at lower risk for cardiovascular disease and those with 3 or more of these cardiovascular risk factors were considered as having a higher cardiovascular risk.

Non-traditional CVRF

Aliquots were frozen at − 80 °C for further determinations of adipocytokines and lipid subfractions [16]. ELISA kits were used for the determination of adiponectin, leptin and E-selectin (Enzo Life Sciences, Farmingdale, NY, USA).

Lipid profiles were characterized by VAP testing (Atherotech, Birmingham, AL, USA), which is an inverted rate zonal, single vertical spin, density gradient ultracentrifugation method to separate lipoproteins into their subclasses [19]. This technique directly measures the total cholesterol in LDL real cholesterol (LDLr-C) and LDL subfractions (LDL-C1–4); VLDL-C (very low-density lipoprotein cholesterol) (VLDL-C1+2 and VLDL3-C); IDL-C (intermediate density lipoprotein); total HDL-C and its subfractions (HDL2-C and HDL3-C); and lipoprotein (a).

Here, we evaluated T-Cholesterol and its subfractions: LDL-C (real LDL-C + IDL-C + Lp (a)-C), IDL-C and the real LDL (LDLr-C), which is biochemically defined by LDL-C fraction from the ultracentrifugation separation of the lipids by VAP. In addition, the following subclasses were evaluated: small dense LDL-C (LDL3-C + LDL4-C); larger buoyant LDL-C (LDL1-C + LDL2-C); VLDL3-C (small dense cholesterol-rich VLDL subfraction); non-HDL-C (non-high-density lipoprotein cholesterol) that corresponds to a sum of LDLr-C, VLDL-C, IDL-C and Lp (a) was analyzed; HDL-C and its sub-fractions HDL2-C (larger, buoyant subclass) and HDL3-C (smaller, denser subclass). Finally, we calculated the logarithm of LDL-C density ratio [LLDR, ln ((LDL3-C + LDL4-C)/(LDL1-C + LDL2-C))], which is closely related to ultracentrifugation-derived LDL density phenotype [18]. Total plasma triglycerides were measured by an enzymatic colorimetric assay (ADVIA 1200, Siemens, Calif., USA).

Statistical analyses

Data were expressed as mean and standard deviation (SD) or as median and interquartile range (IQR) according to continuous variables distribution. Individuals were stratified into quartiles of neck circumference (Men: first quartile, Q1, < 36.5 cm; second quartile, Q2, 36.5 to < 37.9 cm; third quartile, Q3, 37.9 to < 39.5 cm; forth quartile, Q4, ≥ 39.5 cm. Women: first quartile, Q1, < 31.4 cm; second quartile, Q2, 31.4 to < 32.5 cm; third quartile, Q3, 32.5 to < 34 cm; fourth quartile, Q4, ≥ 34 cm). Continuous and categorical variables were compared across NC quartiles using ANOVA and the Chi square test, respectively.

Multiple linear regression models were built to test the associations of NC with biomarkers, lipids and its subfractions, adjusted for age, in total sample, and according to the low-risk group. To evaluate the behavior of this association in individuals at different levels of cardiovascular risk, the linear regression analysis was also stratified by the presence of up to 2 cardiovascular risk factors. Statistical analyses were performed using the Statistical Package for Social Sciences, version 19.0 for Windows (SPSS Inc., Chicago, Illinois, USA). A p value < 0.05 was considered significant.

Results

Of the 807 individuals, 441 were women. Table 1 shows the comparison of mean (SD) or median (IQR) of the risk factors and subfractions of cholesterol across the NC quartiles according to sex. In both sexes, most cardiovascular risk factors such as WC, BMI, systolic and diastolic blood pressure, seric creatinine, fasting and 2-h plasma glucose, HOMA-IR triglycerides increased gradually across the NC quartiles but total cholesterol and total and real LDL-cholesterol (Table 1). HDL-C levels and estimated glomerular filtration rate fasting and 2-h plasma glucose, HOMA-IR triglycerides increased gradually across the NC quartiles but not total cholesterol and total and real LDL-cholesterol (Table 1). HDL-C levels were inversely associated with NC quartiles in both sexes with borderline significance. Leptin, E-selectin, small dense LDL-C, IDL-C, VLDL3-C and TG/HDL ratio increased, and HDL2-C and HDL3-C decreased, while adiponectin, large LDL and log LDL-DR did not differ across the quartiles.
Table 1

Characteristics of participants stratified according to sex and neck circumference

 

Men

N = 366

Women

N = 441

Q1

n = 89

Q2

n = 93

Q3

n = 86

Q4

n = 98

p-value

Q1

n = 104

Q2

n = 106

Q3

n = 112

Q4

n = 119

p-value

Age (years)

45 (5)

45 (5)

45 (4)

46 (5)

0.789

45 (5)

46 (4)

46 (5)

46 (5)

0.320

BMI (kg/m2)

22.3 (2.4)

24.7 (2.3)

26.1 (2.3)

27.4 (1.6)

< 0.001*

22.3 (2.2)

24.2 (2.5)

25.2 92.0)Ω ¥

26.7 (2.1)Ω ¥ π

< 0.001

Waist circumference (cm)

80.5 (7.3)

86.3 (6.5)Ω

89.9 (6.6)Ω ¥

94.5 (5.2)Ω ¥ π

< 0.001*

73.7 (6.0)

78.2 (6.8)

80.9 (6.0)Ω ¥

85.0 (6.3)Ω ¥ π

< 0.001*

Systolic BP (mmHg)

118 (12)

119 (12)

123 (15)

125 (13)Ω

0.002*

108 (12)

110 (13)

112 (13)

113 (12)Ω

0.014*

Diastolic BP (mmHg)

74 (9)

77 (10)

77 (10)

81 (9)Ω¥

< 0.001*

69 (9)

71 (9)

72 (9)

73 (9)Ω

0.005*

Creatinine (mg/dL)

1.02 (0.15)

1.09 (0.15)

1.05 (0.15)

1.11 (0.42)

0.035

0.78 (0.11)

0.81 (0.12)

0.85 (0.14)

0.84 (0.13)

< 0.001*

Glomerular filtration rate (GFR)

90.3 (13.9)

84.6 (14.4)

89.4 (15.2)

86.0 (17.2)

0.073

94.1 (14.5)

89.8 (15.3)

86.7 (16.5)

87.3 (15.4)

0.002*

Fasting glucose (mg/dL)

102.8 (7.9)

103.8 (7.9)

105.2 (8.1)

105.6 (8.3)

0.064*

97.8 (6.4)

99.3 (7.8)

100.9 (7.2)Ω

100.9 (7.2)Ω

0.004*

2-h glucose (mg/dL)

11.6 (6.5)

115.6 (25.5)

121.5 (29.6)

123.1 (26.9)Ω

0.016*

112.4 (24.0)

116.8 (22.8)

118.2 (21.6)

123.3 (26.0)Ω

0.009*

HOMA-IR

0.8 (0.3 to 1.5)

1.3 (0.9 to 2.1)

1.7 (0.1 to 2.4)

1.9 (1.3 to 2.9)

< 0.001*

0.9 (0.5 to 1.5)

1.1 (0.7 to 1.7)

1.3 (0.7 to 2.0)

1.7 (1.1 to 2.5)

< 0.001*

Leptin (ng/mL)

5.6 (2.3 to 10.7)

6.7 (3.9 to 12.6)

7.2 (4.8 to 11.4)

7.9 (5.3 to 12.1)

0.002*

12.4 (7.8 to 21.3)

15.2 (8.1 to 24.3)

17.6 (10.6 to 29.1)

18.8 (12.1 to 30.1)

0.001*

Adiponectin (mcg/mL)

10.3 (6.2 to 15.1)

9.8 (5.8 to 13.7)

9.6 (6.5 to 13.4)

6.5 (4.0 to 12.2)

0.216

11.8 (7.6 to 16.8)

10.6 (6.0 to 15.2)

9.4 (5.6 to 13.9)

9.7 (4.9 to 15.5)

0.358

E-selectin (ng/mL)

61.1 (39.0 to 105.8)

77.8 (51.2 to 127.1)

80.5 (41.5 to 114.4)

87.8 (65.2 to 131.6)

0.006*

61.9 (38.5 to 92.8)

62.1 (37.5 to 99.8)

66.0 (38.2 to 114.2)

76.4 (47.6 to 120.5)

0.162*

Total cholesterol# (mg/dL)

211 (187 to 235)

206 (182 to 231)

219 (188 to 239)

218 (194 to 243)

0.156

206 (185 to 238)

215 (192 to 238)

207 (186 to 242)

213 (189 to 143)

0.761

Non HDL-cholesterol# (mg/dL)

159 (128 to 181)

160 (132 to 179)

165 (137 to 185)

168 (146 to 192)

0.001*

144 (123 to 169)

155 (129 to 171)

149 (126 to 178)

155 (131 to 188)

0.140

HDL-cholesterol# (mg/dL)

53.0 (45 to 62)

48 (42 to 54)

47 (40 to 55)

46 (41 to 53)

0.059*

63 (57 to 70)

60 (52 to 69)

58 (49 to 66)

54 (47 to 68)

< 0.001*

LDL-total# (mg/dL)

138 (111 to 149)

134 (104 to 151)

135 (111 to 158)

138 (116 to 157)

0.527

111 (102 to 145)

134 (107 to 147)

124 (107 to 152)

130 (107 to 156)

0.493

LDL R# (mg/dL)

116 (92 to 129)

109 (86 to 125)

112 (88 to 132)

114 (93 to 129)

0.672

101 (82 to 122)

108 (85 to 124)

105 (88 to 125)

107 (90 to 130)

0.602

IDL-cholesterol# (mg/dL)

14 (10 to 19)

15 (12 to 21)

17 (12 to 22)

18 (14 to 22)

0.003*

14 (10 to 18)

13 (11 to 20)

13 (9 to 18)

15 (11-23)

0.012

Triglycerides# (mg/dL)

92 (71 to 127)

108 (86 to 140)

114 (89 to 174)

146 (96 to 193)

< 0.001*

76 (61 to 98)

84 (70 to 115)

82 (67 to 110)

106 (73 to 144)

< 0.001*

HDL2-C# (mg/dL)

13.0 (10 to 17)

11 (8 to 14)

11 (8 to 15)

11 (9 to 13)

0.001*

19 (16 to 24)

18 (14 to 22)

16 (13 to 20)

15 (12 to 20)

< 0.001*

HDL3-C# (mg/dL)

39 (34 to 46)

36 (33 to 40)

37 (31 to 41)

36 (32 to 41)

0.008

43 (40 to 48)

41 (38 to 47)

41 (37 to 46)

40 (36 to 46)

0.002

Large LDL-C# (mg/dL)

40 (30 to 49)

39 (31 to 49)

40 (33 to 50)

39 (32 to 52)

0.763*

43 (30 to 61)

44 (31 to 58)

41 (29 to 55)

42 (33 to 59)

0.723

Small LDL-C# (mg/dL)

53 (38 to 79)

60 (37 to 79)

62 (42 to 81)

72 (48 to 88)

0.060*

38 (29 to 49)

41 (31 to 54)

44 (32 to 61)

47 (33 to 64)

0.005*

Log LDL DR# (mg/dL)

0.37 (0.02 to 0.73)

0.48 (0.09 to 0.72)

0.52 (0.13 to 0.73)

0.59 (0.17 to 0.81)

0.295

− 0.17 (− 0.60 to 0.36)

− 0.04 (− 0.41 to 0.33)

0.14 (− 0.31 to 0.58)

0.20 (− 0.27 to 0.53)

0.012*

VLDL3-C# (mg/dL)

13 (10 to 15)

15 (12 to 18)

14 (13 to 17)

16 (13 to 20)

< 0.001

12 (9 to 14)

12 (11 to 15)

12 (10 to 14)

14 (11 to 17)

< 0.001

TG/HDL ratio

1.7 (1.2 to 2.7)

2.4 (1.7 to 3.3)

2.4 (1.6 to 4.0)

3.1 (2.0 to 4.5)

< 0.001

1.2 (0.9 to 1.6)

1.4 (1.0 to 2.0)

1.4 (1.1 to 2.2)

1.9 (1.2 to 2.7)

< 0.001

BMI, body mass index NC, neck circumference BP, blood pressure

Glomerular filtration rate (GFR) was estimated using the formula of Chronic Kidney Disease Epidemiology Collaboration—CKD-EPI

Lipid profile evaluated by Vertical Auto Profile

Values are means (SD) or medians (interquartile intervals)

Men: first quartile, Q1, < 36.5 cm; second quartile, Q2, 36.5 to < 37.9 cm; third quartile, Q3, 37.9 to < 39.5 cm; forth quartile, Q4, ≥ 39.5 cm. Women: first quartile, Q1, < 31.4 cm; second quartile, Q2, 31.4 to < 32.5 cm; third quartile, Q3, 32.5 to < 34 cm; forth quartile, Q4, ≥ 34 cm

p value, ANOVA was used. Ω versus Q1; ¥ versus Q2; π versus Q3. # Kruskal–Wallis test was used. * p for trend < 0.05

The frequency of central obesity defined by WC was 4.9% and 14.3%, hypertension 24.3% and 12.0%, hypertriglyceridemia 32.8% and 14.5%, low HDL-cholesterol levels 13.7% and 19.7% and pre-diabetes 75.1% and 54.2% in men and women, respectively. Those who had up to 2 traditional CVRF were considered at low-risk (83% men and 89% women). The prevalence of having 3 or more traditional CVRF increased across the groups of neck quartiles (Q1: 6.7% and 2.9%; Q2: 9.8% and 7.8%; Q3: 21.2% and 11.7%; Q4: 30.6% and 19.8%) in both men and women respectively. For each age-adjusted 1 cm increase in NC, changes of + 2.3 cm in waist circumference, + 0.85 kg/m2 in BMI, + 0.5 mg/dL in fasting glucose and + 0.9 mmHg in systolic blood pressure for men and women were observed.

Biomarkers related to atherogenesis—leptin, E-selectin, small LDL and HDL2—increased across the NC quartiles for each sex are shown in Fig. 1. There is a tendency for worsening of the lipid profile according to an increase of NC was observed.
Fig. 1
Fig. 1

Mean (95% CI) values of leptin, E-selectin, small LDL-C, HDL2-C according to neck circumference in men and women

Due to the pattern observed for leptin, E-selectin, small-dense LDL and HDL2 concentrations across the NC categories, the association between each of these variables was examined in linear regression analysis. A direct and independent association of NC with leptin, E-selectin and small-dense LDL and an inverse independent association with adiponectin and HDL2 were detected for the entire sample and also for individuals of both sexes with ≤ 2 risk factors (Table 2). For the group of participants with ≥ 3 cardiovascular risk factors the results were not significant.
Table 2

Association of leptin, E-selectin and sub fractions of lipoprotein with neck circumference according to sex, considering the entire sample and individuals at low cardiovascular risk

 

Leptin

E-selectin

Small-dense LDL-C

HDL2-C

β

95% CI

p

β

95% CI

p

β

95% CI

p

β

95% CI

p

Entire sample

 Men

  Neck circumference

0.155

0.069 to 0.242

< 0.001

0.105

0.032 to 0.177

0.005

1.866

0.641 to 3.091

0.003

− 0.519

− 0.773 to − 0.266

< 0.001

 Women

  Neck circumference

0.147

0.075 to 0.220

< 0.001

0.73

0.006 to 0.140

0.032

2.372

1.391 to 3.353

< 0.001

− 0.815

− 1.115 to − 0.515

< 0.001

Lower risk subsample

 Men

  Neck circumference

0.183

0.084 to 0.282

< 0.001

0.95

0.015 to 0.175

0.021

1.758

0.478 to 3.039

0.007

− 0.476

− 0.766 to − 0.186

0.001

 Women

  Neck circumference

0.152

0.075 to 0.229

< 0.001

0.068

− 0.004 to 0.140

0.066

1.620

0.597 to 2.643

0.002

− 0.685

− 1.013 to − 0.356

< 0.001

Higher risk subsample

 Men

  Neck circumference

0.001

− 0.103 to 0.103

0.995

0.074

− 0.033 to 0.181

0.173

− 2.520

− 6.299 to 1.260

0.187

0.165

− 0.273 to 0.640

0.454

 Women

  Neck circumference

− 0.014

− 0.068 to 0.041

0.619

0.015

− 0.034 to 0.064

0.545

1.610

− 1.484 to 4.705

0.300

− 0.076

− 0.514 to 0.667

0.796

Linear regression analysis. Adjusted for age. Lower risk subsample—individuals with up to 2 cardiovascular risk factors. Higher risk subsample—3 or more cardiovascular risk factors. Traditional cardiovascular risk factors: (1) waist circumference ≥ 102 cm for men or ≥ 88 cm for women; (2) systolic or diastolic blood pressure ≥ 130/85 mmHg or antihypertensive treatment; (3) fasting plasma glucose ≥ 100 mg/dL and < 126 mg/dL in the absence of antidiabetic agents; (4) triglyceride ≥ 150 mg/dL, or specific treatment; (5) HDL-cholesterol < 40 mg/dL for men and < 50 mg/dL for women, or specific treatment

Sensitivity analyses, excluding participants under medications (antihypertensive and/or lipid reducing agents and/or hormone therapy), current smoking and menopause were performed but results did not change.

Discussion

Our findings showed the ability of NC to identify a risk profile, including non-traditional biomarkers such as leptin, E-selectin and lipoprotein subfractions, in non-obese individuals, considered at low-to-moderate cardiovascular risk.

Despite the recognized impact of traditional cardiovascular risk factors such as increased age, hypertension, smoking, diabetes and abnormalities in lipoprotein metabolism [20], a considerable proportion of individuals without these factors sustain cardiovascular events. Considering the multiplicity of factors involved in atherogenesis, measurement of markers of endothelial dysfunction and insulin resistance could help identify subsets of patients at increased cardiovascular risk.

Some studies have already reported the association of NC with cardiovascular risk factors, namely central obesity, hypertension, insulin resistance, and dyslipidemia in individuals at higher cardiovascular risk [4, 2123]. NC was suggested as a risk factor independent of adipose tissue mass and distribution [2]. Furthermore, NC was found to be a predictor of fatal and non-fatal cardiovascular events as well as of renal dysfunction in a sample of high-risk patients, [24, 25], but we are not aware of studies that have evaluated the role of NC in low risk individuals as we did. In the present study, even in non-obese individuals, increments in waist circumference, blood pressure, and plasma glucose values were observed across NC quartiles, while HDL-cholesterol and estimated glomerular filtration rate decreased. We emphasize that the mean values of these traditional cardiovascular risk factors, creatinine and estimated glomerular filtration rate, were within normal ranges.

A gradual increase of leptin and decrease of adiponectin levels were detected across the NC quartiles. Previous studies have shown that low adiponectin and high leptin levels were associated with a pro-inflammatory state, insulin resistance and coronary artery calcium severity in adults [4, 26]. It was suggested that NC could be a predictor of low-grade systemic inflammation in adults as it was reported in children [27]. Our findings support the assumption that NC could identify low-to-moderate risk individuals who already have a worse profile of adipocytokines related to insulin resistance and low grade inflammation.

Leukocyte-endothelial cell adhesion molecules, such as E-selectin, are also expressed in response to cytokines and play a role in the atherogenesis [28]. Interestingly, we found a linear increase in E-selectin concentrations in parallel to increases in NC as shown in Fig. 1 (p for trend = 0.039 for women and 0.004 for men). In a previous analysis of the ELSA-Brasil, we reported that E-selectin was associated with insulin resistance and the presence of calcium in coronary arteries in individuals without diabetes or cardiovascular disease [29]. We proposed that higher circulating E-selectin levels, found across the quartiles of NC, might be suggestive of early atherogenesis, as well as of an early disturbance of glucose metabolism.

We used the VAP measurements to provide information beyond the basic lipid profile and may help identify individuals at higher cardiovascular risk. Small dense LDL are biophysically more likely to access the subendothelial space and more prone to oxidation. It is known that hypertriglyceridemia and low HDL-C are associated with a predominance of small dense LDL-C (LDL3-C and LDL4-C) particles (also known as phenotype B) [30, 31]. Increased hepatic lipase activity and triglyceride enrichment of lipoproteins are commonly found in states of insulin resistance, resulting in a reduction of HDL-C—predominantly the HDL2-C subclass—and a relative or absolute increase in the small dense HDL3-C [31, 32]. This scenario accelerates atherogenesis, contributing to elevate cardiovascular risk. In this context, NC was able to indicate similar lipoprotein subfractions alterations (HDL2-C reduction, TG/HDL ratio elevation) that are strongly associated with insulin resistance and small LDL. In addition, it was directly associated with small-dense LDL-C and negatively with HDL2-C, maintaining significance even when including only low risk individuals. Therefore, we hypothesize that NC might be a useful proxy of early lipid profile disturbances involved in atherogenesis.

It is well known the importance of other anthropometric measurements, such as BMI and waist circumference, to predict cardiometabolic risk and the actual study provide evidence that NC could be another anthropometric measurement to identify early atherogenic profile. To emphasize the usefulness of NC, this study evaluated a sample constituted by non-obese individuals (BMI up to 30) and regression analysis were stratified according to the number of CV risk factors. Waist circumference did not enter in final models of multiple regression analysis to avoid over adjustments, since NC is associated with waist and both may represent the association between adiposity and cardiometabolic risk. Comparing to waist circumference, neck circumference might be an easier performed anthropometric measurement for clinical practice.

The cross-sectional design of this study limits concluding that increased NC is causal for the associations described herein. Increased NC is highly correlated with increased insulin resistance, a driving force for many of the metabolic alterations we found. Significant associations of NC with non-traditional risk factors were not detected in our linear regression analyses, and two possible explanations were considered: the number of individuals with 3 or more cardiovascular risk factors (63 men and 47 women) could be not sufficient to reach statistical significance. Also, we could hypothesize that the risk of individuals with ≥ 3 major risk factors is already so high that elevated non-traditional risk factors do not differ among the NC quartiles anymore.

Strengths of our study were its large sample of low-moderate risk individuals and the analysis of novel circulating markers of initial arterial damage. The follow-up of these ELSA-Brasil participants should allow us to test the hypothesis raised in this present study.

In conclusion, this study verified that non-obese individuals with higher neck circumference demonstrate more abnormalities in traditional risk factors and non-traditional risk factors such as leptin, adiponectin and selectin as well as a more atherogenic lipid profile even in a low-risk group. Neck circumference is an easily performed anthropometric measurement that may potentiate early identification of those individuals at low-to-moderate risk in whom markers of atherogenesis are readily detected.

Declarations

Authors’ contributions

BAP participated in the study design, organization of the data, analysis of novel biomarkers, statistical analysis, and interpretation of the results and draft the article. ITS and MIHF participated in the interpretation of data and manuscript drafting. ACG participated in the review of the statistical analysis and of the article. MSB, MB, PT, SJ, KK, RS were responsible for the VAP analysis and review of the paper. IMB conceived of the ELSA-Brasil study, participated in interpretation of the results and review of the article. PAL conceived of the ELSA-Brasil study, participated in interpretation of the results and review of the article. SRGF conceived of the actual study, design of the study, participated in interpretation of the results and review of the article. All authors read and approved the final manuscript.

Acknowledgements

The ELSA-Brasil baseline study was supported by the Brazilian Ministry of Health (Science and Technology Department) and the Brazilian Ministry of Science and Technology.

Competing interests

Bianca de Almeida Pititto, Isis Tande da Silva, Marilia L Fonseca, Alessandra C. Goulart, Michael J. Blaha, Steven Jones, Isabela M. Benseñor, Paulo A. Lotufo and Sandra R G Ferreira declare that they have no competing interests. Marcio S. Bittencourt has received honoraria for consulting and speaker activities from Boston Scientific and research grant support from Sanofi. Raul D. Santos has received honoraria for consulting, speaker activities and research activities from: Amgen, Akcea, Astra Zeneca, Biolab, Kowa, Esperion, Merck, Pfizer, Novo-Nordisk and Sanofi/Regeneron. Peter Toth has received honoraria for consulting or being a member of the speakers’ bureau for Amarin, Amgen, AstraZeneca, Kowa, Merck, and Regeneron-Sanofi. Krishnaji Kulkarni has received honoraria from VAP Diagnostics Laboratory.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The School of Public Health of University of São Paulo Ethics Committee approved the study and written consent was obtained from all participants.

Funding

The ELSA-Brasil baseline study was supported by CNPq-National Research Council (Grants # 01 06 0010.00 RS, 01 06 0212.00 BA, 01 06 0300.00 ES, 01 06 0278.00 MG, 01 06 0115.00 SP, 01 06 0071.00 RJ).

The current work was supported by grant from the São Paulo Research Foundation (Fundação de Amparo à Pesquisa do Estado de São Paulo—FAPESP—Protocol 2010/00074-6), São Paulo, SP, Brazil.

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Authors’ Affiliations

(1)
Department of Preventive Medicine, Federal University of Sao Paulo, Rua Botucatu 740, São Paulo, SP, 04023900, Brazil
(2)
School of Public Health, University of São Paulo, Av. Dr. Arnaldo, 715, São Paulo, SP, 01246-904, Brazil
(3)
Department of Internal Medicine, University of São Paulo, Av. Lineu Prestes 2565, 4th Floor, São Paulo, SP, 05508-000, Brazil
(4)
Center for Clinical and Epidemiological Research, Hospital Universitário, University of São Paulo, Av. Lineu Prestes 2565, 3rd Floor, São Paulo, 05508-000, Brazil
(5)
Lipid Clinic Heart Institute (InCor), University of Sao Paulo, Medical School Hospital, Av. Dr. Enéas de Carvalho Aguiar 44, São Paulo, 01246-000, Brazil
(6)
Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Blalock 524 D1, 600 N. Wolfe St, Baltimore, MD, USA
(7)
Department of Preventive Cardiology, CGH Medical Center, 100 E. Le Fevre Road, Sterling, IL 61081, USA
(8)
VAP Diagnostics Laboratory, 201 London Pkwy, Birmingham, AL 35211, USA

References

  1. Christopher JL, Murray CJL, Lopez AD. Measuring the global burden of disease. N Engl J Med. 2013;369:448–57. https://doi.org/10.1056/NEJMra1201534.View ArticleGoogle Scholar
  2. Preis SR, Massaro JM, Hoffmann U, D’Agostino RB, Levy D, Robins SJ, Meigs JB, Vasan RS, O’Donnell CJ, Fox CS, et al. Neck circumference as a novel measure of cardiometabolic risk: the Framingham Heart study. J Clin Endocrinol Metab. 2010;95:3701–10.View ArticleGoogle Scholar
  3. Ben-Noun L, Laor A. Relationship of neck circumference to cardiovascular risk factors. Obes Res. 2003;11:226–31.View ArticleGoogle Scholar
  4. Baena CP, Lotufo PA, Santos IS, Goulart AC, Bittencourt MS, Duncan BB, Liu S, Benseñor IM. Neck circumference is associated with carotid intimal-media thickness but not with coronary artery calcium: results from the ELSA-Brasil. Nutr Metab Cardiovasc Dis. 2016;26:216–22.View ArticleGoogle Scholar
  5. Santos IS, Alencar AP, Rundek T, Goulart AC, Barreto SM, Pereira AC, Benseñor IM, Lotufo PA. Low impact of traditional risk factors on carotid intima-media thickness—the ELSA-Brasil Cohort. http://atvb.ahajournals.org/content/early/2015/07/16/ATVBAHA.115.305765. Accessed 19 Apr 2017.
  6. Calder PC, Ahluwaia N, Albers R, Bosco N, Sicard-Bourdet R, Haller D, Holgate ST, Jonsson LS, Latulippe ME, Marcos A, Moreines J, M’Rini C, Muller M, Pawelec G, van Neerven RJJ, Watzl B, Zhao J. A consideration of biomarkers to be used for evaluation of inflammation in human nutritional studies. Brit J Nutr. 2013;109(S1):S1–34.View ArticleGoogle Scholar
  7. Ridker PM, Hennekens CH, Buring JE, Rifai N. C-Reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 2000;342:836–43.View ArticleGoogle Scholar
  8. Meigs JB, Hu FB, Rifai N, Manson JE. Biomarkers of endothelial dysfunction and risk of type 2 diabetes mellitus. JAMA. 2004;291(16):1978–86. https://doi.org/10.1001/jama.291.16.1978.View ArticlePubMedGoogle Scholar
  9. Almeida-Pititto B, Ribeiro-Fiçho FF, Bittencourt MS, Bensenor IM, Lotufo PA, Ferreira SRG. Usefulness of circulating E-selectin to early detection of the atherosclerotic process in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Diabetol Metab Syndr. 2016;8:19–25. https://doi.org/10.1186/s13098-016-0133-9.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Lindberg S, Jensen JS, Bjerre M, Oedersen SH, Frystyk J, Flyvbjerg A, Galatius S, Jeppesen J, Mogelvang R. Adiponectin, type 2 diabetes and cardiovascular risk. Eur J Prev Cardiol. 2015;22(3):276–83.View ArticleGoogle Scholar
  11. Martin SS, Qasim A, Reilly MP. Leptin resistance: a possible interface of inflammation and metabolism in obesity-related cardiovascular disease. J Am Coll Cardiol. 2008;52(15):1201–10.View ArticleGoogle Scholar
  12. Mora S. Advanced lipoprotein testing and subfractionation are not (yet) ready for routine clinical use. Circulation. 2009;119:2396–404.View ArticleGoogle Scholar
  13. Zhang Y, Zhu CG, Xu RX, Li S, Li XL, Guo YL, Wu NQ, Gao Y, Qing P, Cui CJ, Sun J, Li JJ. HDL subfractions and very early CAD: novel findings from untreated patients in a Chinese cohort. Sci Rep. 2016;6:30741.View ArticleGoogle Scholar
  14. Aquino EML, Barreto SM, Bensenor IM, Carvalho MS, Chor D, Duncan B, Lotufo PA, Mill JG, Molina MDC, Mota ELA, Passos VMA, Schmidt MI, Szklo M. Brazilian longitudinal study of adult health (ELSA-Brasil): objectives and design. Am J Epidemiol. 2012;175(4):315–24.View ArticleGoogle Scholar
  15. Mill JG, Karina Pinto K, Griep RH, Goulart A, Foppa M, Lotufo PA, Maestri MK, Ribeiro AL, Andreão RV, Dantas EM, Oliveir I, Fuchs SC, Cunha RS, Bensenor IM. Medical assessments and measurements in ELSA-Brazil. Rev Saúde Pública. 2013;47(Supl 2):54–62.View ArticleGoogle Scholar
  16. Lohman TG, Roche AF, Martorell R, editors. Anthropometric standardization reference manual. Champaign: Human Kinetics Publications; 1988.Google Scholar
  17. Fedeli LG, Vidigal PG, Leite CM, Castilhos CD, Pimentel RA, Maniero VC, Mill JG, Lotufo PA, Pereira AC, Bensenor IM. Logistics of collection and transportation of biological samples and the organization of the central laboratory in the ELSA-Brasil. Rev Saude Publica. 2013;47(Suppl 2):63–71 (Portuguese).View ArticleGoogle Scholar
  18. Kidney Disease Improving Global Outcomes. Clinical practice guideline for the evaluation and management of chronic kidney disease. Off J Int Soc Nephrol. 2013;3(1):7–10.Google Scholar
  19. Kulkarni KR. Cholesterol profile measurement by vertical auto profile method. Clin Lab Med. 2006;26:787–802.View ArticleGoogle Scholar
  20. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA. 2001;285(19):2486–97. https://doi.org/10.1001/jama.285.19.2486.View ArticleGoogle Scholar
  21. Stabe C, Vasques ACJ, Lima MMO, et al. Neck circumference as a simple tool for identifying the metabolic syndrome and insulin resistance: results from the Brazilian Metabolic Syndrome Study. Clin Endocrinol (Oxf). 2013;78:874–81.View ArticleGoogle Scholar
  22. Dai Y, Wan X, Li X, Jin E, Li X. Neck circumference and future cardiovascular events in a high-risk population—A prospective cohort study. Lipids Health Dis. 2016;5(15):46. https://doi.org/10.1186/s12944-016-0218-3.View ArticleGoogle Scholar
  23. Ma C, Wang R, Liu Y, et al. Diagnostic performance of neck circumference to indentify overweight and obesity as defined by body mass index in children and adolescents: systematic review and meta-analysis. Ann Hum Biol. 2016;1:1. https://doi.org/10.1080/03014460.2016.1224387.View ArticleGoogle Scholar
  24. Khalangot M, Gurianov V, Okhrimenko N, Luzanchuk I, Kravchenko V. Neck circumference as a risk factor of screen-detected diabetes mellitus: community-based study. Diabetol Metab Syndr. 2016;8:12. https://doi.org/10.1186/s13098-016-0129-5.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Liu Y, Chang S, Lin W, Hsu J, Chung C, Chang J, Hung K, Chen K, Chang C, Chen F, Shih Y, Chu C. Neck circumference as a predictive indicator of CKD for high cardiovascular risk patients. Bio Med Res Int. 2015. https://doi.org/10.1155/2015/745410.View ArticleGoogle Scholar
  26. Almeida-Pititto B, Ribeiro-Fiçho FF, Santos IS, Bensenor IM, Lotufo PA, Ferreira SRG. Association between carotid intima-media thickness and adiponectin in participants without diabetes or cardiovascular disease of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Eur J Prev Cardiol. 2016. https://doi.org/10.1177/2047487316665490.View ArticlePubMedGoogle Scholar
  27. Castro-Piñero J, Delgado-Alfonso A, Marco LG, Gómez-Martínez S, Esteban-Cornejo I, Veiga OL, Marcos A, The UP&DOWN Study Group. Neck circumference and clustered cardiovascular risk factors in children and adolescents: cross sectional study. BMJ Open. 2017;7:E016048. https://doi.org/10.1136/bmjopen-2017-016048.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Hwang S-J, Ballantyne CM, Sharrett AR, Smith LC, Davis CE, Gotto AM, Boerwinkle E. Circulating adhesion molecules VCAM-1, ICAM-1, and E-selectin in carotid atherosclerosis and incident coronary heart disease cases. The atherosclerosis risk in communities (ARIC) study. Circulation. 1997;96:4219–25.View ArticleGoogle Scholar
  29. Shapiro MD, Fazio S. From lipids to inflammation: new approaches to reducing atherosclerotic risk. Circ Res. 2016;118(4):732–49. https://doi.org/10.1161/CIRCRESAHA.115.306471.View ArticlePubMedGoogle Scholar
  30. Sánchez-Quesada JL, Vinagre I, De Juan-Franco E, et al. Impact of the LDL subfraction phenotype on Lp-PLA2 distribution, LDL modification and HDL composition in type 2 diabetes. Cardiovasc Diabetol. 2013;12:112.View ArticleGoogle Scholar
  31. Hoogeveen RC, Gaubatz JW, Sun W, Dodge RC, Crosby JR, Jiang J, et al. Small dense low-density lipoprotein-cholesterol concentrations predict risk for coronary heart disease: the Atherosclerosis Risk In Communities (ARIC) study. Arterioscler Thromb Vasc Biol. 2014;34(5):1069–77.View ArticleGoogle Scholar
  32. Xu RX, Li S, Li XL, Zhang Y, Guo YL, Zhu CG, Wu NQ, Qing P, Sun J, Dong Q, Li JJ. High-density lipoprotein subfractions in relation with the severity of coronary artery disease: a Gensini score assessment. J Clin Lipidol. 2015;9(1):26–34. https://doi.org/10.1016/j.jacl.2014.11.003.View ArticlePubMedGoogle Scholar

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© The Author(s) 2018

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