Skip to content

Advertisement

  • Review
  • Open Access

Neck circumference and its association with cardiometabolic risk factors: a systematic review and meta-analysis

  • 1,
  • 2,
  • 3, 4,
  • 5,
  • 6,
  • 1,
  • 7,
  • 8,
  • 9,
  • 10 and
  • 6, 11Email author
Diabetology & Metabolic Syndrome201810:72

https://doi.org/10.1186/s13098-018-0373-y

  • Received: 8 July 2018
  • Accepted: 14 September 2018
  • Published:

Abstract

Background

Recently, neck circumference (NC) has been used to predict the risk of cardiometabolic factors. This study aimed to perform a systematic review and meta-analysis to examine: (i) the sensitivity (SE) and specificity (SP) of NC to predict cardiometabolic risk factors and (ii) the association between NC and the risk of cardiometabolic parameters.

Methods

A systematic search was conducted through PubMed/Medline, Institute of Scientific Information, and Scopus, until 2017 based on the search terms of metabolic syndrome (MetS) and cardio metabolic risk factors. Random-effect model was used to perform a meta-analysis and estimate the pooled SE, SP and correlation coefficient (CC).

Results

A total of 41 full texts were selected for systematic review. The pooled SE of greater NC to predict MetS was 65% (95% CI 58, 72) and 77% (95% CI 55, 99) in adult and children, respectively. Additionally, the pooled SP was 66% (95% CI 60, 72) and 66% (95% CI 48, 84) in adult and children, respectively. According to the results of meta-analysis in adults, NC had a positive and significant correlation with fasting blood sugar (FBS) (CC: 0.16, 95% CI 0.13, 0.20), HOMA-IR (0.38, 95% CI 0.25, 0.50), total cholesterol (TC) (0.07 95% CI 0.02, 0.12), triglyceride (TG) concentrations (0.23, 95% CI 0.19, 0.28) and low density lipoprotein cholesterol (LDL-C) (0.14, 95% CI 0.07, 0.22). Among children, NC was positively associated with FBS (CC: 0.12, 95% CI 0.07, 0.16), TG (CC: 0.21, 95% CI 0.17, 0.25), and TC concentrations (CC: 0.07, 95% CI 0.02, 0.12). However, it was not significant for LDL-C.

Conclusion

NC has a good predictive value to identify some cardiometabolic risk factors. There was a positive association between high NC and most cardiometabolic risk factors. However due to high heterogeneity, findings should be declared with caution.

Keywords

  • Neck circumference
  • Metabolic syndrome
  • Cardiometabolic risk factors

Background

Cardiovascular diseases are dominant cause of death across the world [1]. Obesity is an important risk factor for these threats and other cardiometabolic diseases such as diabetes [2].

The association between body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR), indices of general or central obesity, with increased cardiometabolic risk has been proved in numerous studies [2, 3]. However, these measures need calibrated tools such as scale, or vary throughout a day. In contrast, neck circumference (NC) is easy to measure, constant, and time-saving measure to identify overweight and obese individuals [4, 5]. It has also been shown as a tool associated with central obesity [6], hypertension and other components of metabolic syndrome (MetS) [7]. A recent meta-analysis from six studies in children and adolescents showed that NC was moderately associated with BMI [8]. To our knowledge, there has been no meta-analysis on sensitivity (SE) and specificity (SP) of NC to identify cardiometabolic risk factors, so far. Moreover, the association between NC and cardiometabolic risk factors has not been examined in child population. Accordingly, we performed a systematic review on studies which assessed NC in association with cardiometabolic risk factors, and studies which reported SE and SP of NC to identify cardiometabolic risk factors.

Methods

This study was designed as a systematic review on the association of NC and cardio metabolic risk factors. The main related international electronic data sources of PubMed and the NLM Gateway (for MEDLINE), Institute of Scientific Information (ISI), and Scopus searched systematically. For each, strategies were run separately regarding the detailed practical instruction including filters and refining processes. The medical subject headings, Entry Terms and Emtree options were used to reach the most sensitive search.

The strategy developed based on the search terms of MetS, cardio metabolic risk that included all of related components such as glycemic indices including diabetes mellitus, blood glucose, hemoglobin A1c (HbA1c), homeostatic model assessment (HOMA), insulin resistance (IR), lipid profiles including triglycerides (TG), low density lipoprotein (LDL), high density lipoprotein cholesterol (HDL-C), total cholesterol (TC), anthropometric measures including body mass index (BMI), waist circumference (WC), NC, overweight, generalized and abdominal obesity, and blood pressure (BP) including systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and their sub-components. At next stage these queries added to results for NC. Data refined for human subject without restriction on language.

We excluded papers of non-population-based studies or those with duplicate citation. For multiple publications of the same population, only the article with largest sample size was included.

The bibliographic information of searched studies saved using Endnote software and four independent reviewers completed all three steps of data refinement, including titles, abstracts and full texts review. Possible disagreements were resolved by third reviewer (M.Qh).

Using Cohen’s kappa statistic, agreement between the results of data extraction of two experts (Sh.D, P.Ch) was 0.94. Data were collected through standard forms which contained author᾽ name, publication year, location, and type of study, sample size, age range, sex, measurements details, and interested outcomes.

Risk of bias assessment

Risk of bias for studies which reported diagnostic accuracy of NC for predicting cardiometabolic risk factors was assessed using “quality assessment of diagnostic accuracy studies 2” (QUADAS2) checklist. This checklist includes four main methodological domains of study (sample selection, index test, gold standard, process and timing). According to this checklist studies were categorized as “low risk of bias”, “high risk of bias” and “unclear”. The quality assessment of observational studies which assessed association between NC and cardiometabolic risk factors was assessed using the Newcastle–Ottawa checklist which is adapted for types of study (cross sectional, case–control, cohort). In this checklist, each study can attain 9 scores for its quality. Four scores for the selection of study groups, two scores for the comparability of the groups, and three scores for the assessment of outcomes. A study with a Newcastle–Ottawa scale score of ≥ 6 was considered as high quality study. Three authors (H.A, M.Z, A.M) independently evaluated the included studies. A third author (M.M) resolved any disagreements between them.

Ethical considerations

The protocol of study was approved by the ethical committee of Alborz University of Medical Science. All reviewed studies were properly cited. For more information about a certain study, we contacted the corresponding authors.

Statistical analysis

The results of diagnostic accuracy of NC to identify MetS was presented as SE, SP and the area under the curve (AUC). The overall (pooled) SE and of SP of NC to identify MetS according to sex and age groups (pediatric and adult) was estimated using random effect meta-analysis method (using the Der-Simonian and Laird method). Forest plot also was used to present result of meta-analysis schematically.

To examine the overall correlation between NC and cardiometabolic risk factors, when r Pearson was reported a mean transformed correlation using r-to-z transformation procedure was used to obtain Fisher’s Z. The standard error was also calculated based on the variance of Fisher’s Z. Spearman was also converted Pearson correlation coefficients, using the following formula:
$${\text{r}}_{{({\text{Pearson}})}} = { 2}*{ \sin }\left( {{\text{r}}_{{({\text{Spearman}})}} *\uppi/ 6} \right)$$
We used Der Simonian and Laird method to pool the correlation coefficients (CC). Between-study heterogeneity was assessed using the I2 statistic and I2 more than 50% considered as high heterogeneity. Findings were reported separately for adults and children. When the heterogeneity was high, we stratified the studies according to mean age (more or less than 48 years), sex (men, women, both) and continent (Asian, non-Asian) in adult populations. As the range of age in children was similar among the included studies, only sex and continent was considered for subgroup analysis. Stratification was performed when at least two studies were in each sub group. To assess publication bias when there were more than 10 effect sizes, funnel plots and Begg test was used. However, publication bias for variables with less than 10 effect sizes was examined using Egger test. P-value < 0.05 value was considered statistically significant. All statistical analyses were performed with Stata version 12.0 (STATA Corp, College Station, TX, USA).

Results

Figure 1 shows the selection process of articles. In total, 657 records were obtained using searching through PubMed and the NLM Gateway (for MEDLINE), ISI, and Scopus. Subsequently, 325 duplicates were removed. Articles were screened by title and abstract. In addition, 4 articles were identified through reference checking. A total of 80 full texts were assessed for eligibility and finally, 41 articles were selected. The topic of target studies were categorized as follow:
Fig. 1
Fig. 1

Flowchart of study selection

  1. i)

    Studying diagnostic accuracy of high NC for prediction cardiometabolic risk factors (n = 21).

     
  2. ii)

    Studying association between NC and cardiometabolic risk factors (n = 33).

     

Some studies addressed both of these topics.

Results of qualitative synthesis

A-1: The diagnostic accuracy of high NC to predict cardiometabolic risk factors

A total of 21 articles (including 18 cross-sectional and 3 case–control studies) had reported SE and SP of NC for prediction of cardiometabolic risk factors. They were published between 2010 and 2016 in different countries: China (n = 4), Brazil (n = 3), USA (n = 3), India (n = 3), and 1 article in Colombia, Ukraine, Europe, Turkey, Canada, and Egypt. Eleven studies included children and adolescents and the other 10 ones assessed adults (Table 1).
Table 1

Characteristics of the included studies which assessed diagnostic value of high neck circumference to predict cardiometabolic risk factors

Study

Type of study

Country

Target population

Sam pie size

Sex ratio (M/F)

Age year

Outco me

Diagnostic criteria

Cut-off values for high NC

Age-sex group

SE % (95% CI)

SP % (95% CI)

AUC (95% CI)

Silva, et al [15]

Cross-sectional

Brazil

Healthy

388

169/219

10–19

Insulin resistance

H0MA1-IR ≥ 3.87 and HOMA1-IR ≤4.19 for females.

H0MA1-IR ≥ 3.85 and HOMA1-IR  ≥ 3.77 for males

Prepubertal females:  > 32.0 cm

Prepubertal females

76.92 (46.2-94.7)

77.50 (61.5–89.1)

0.84 (0.72–0.97)

Pubertal females:  > 34.1 cm

Pubertal females

56.41 (39.6–72.2)

84.75 (77.0–90.7)

0.76 (0.68–0.85)

Prepubertal males:  > 30.3 cm

Prepubertal males

100.00 (78.0–100.0)

42.55 (28.3–57.8)

0.72 (0.58–0.86)

Pubertal males:  > 34.8 cm

Pubertal males

92.00 (73.9–98.8)

57.33 (45.4–68.7)

0.81 (0.71–0.91)

Goncalves et al. [12]

Cross sectional

Brazil

Healthy

260

129/131

10–14

BP

High BP:  > 95th percentile

Total:30.2 cm, Male: 30.5 cm. Female: 29.9 cm

Total

80.0

78.4

0.807 (0.754–0.854)

Female

100.0

69.5

0.908 (0.844–0.951)

Male

85.7

71.3

0.747 (0.663–0.819)

TG

Triglycerides  ≥ 100 mg/dL

Male

54.6

86.4

0.70 (0.613–0.777)

HDL-C

HDL-C  < 45 mg/dL

Both sexes

61.9

59.3

0.616 (0.554–0.676)

Female

64.5

64.0

0.645 (0.557–0.727)

Insulin resistance

Fasting insulin  ≥ 15 uU/mL

Both sexes

72.7

57.3

0.703 (0.643–0.757)

Female

95.7

36.1

0.659 (0.571–0.739); p < 0.05

Male

100.0

74.0

0.902 (0.837–0.947); p < 0.001

Diabet es

Fasting glucose  ≥ 100 mg/dL

Total

080.0

67.5

0.682 (0.621–0.738); p < 0.001

Female

100.0

75.8

0.827 (0.751–0.887); p < 0.001

Excess body fat

Body fat 15–25% for females and 10–20% for males

Total

64.0

65.1

67.6 (0.615–0.733); p < 0.001

Female

67.6

66.7

0.711 (0.625–0.786); p < 0.001

Male

75.0

60.7

0.728 (0.643–0.803); p < 0.001

Torriani et al. [37]

Cross-sectiona

USA

Subjects with treated malignancies

303

152/151

18–91

MetS

NCEP Adult Treatment Panel III

43.6 cm in men and 38.6 cm in women

Male

74 (60, 85)

80 (66, 90)

0.79 (0.70, 0.86)

Female

74 (60, 85)

91 (80, 97)

0.85 (0.76, 0.91)

Formisano et al. [23]

Cross-sectiona

Italy, Belgium, Cyprus, Estonia, Germany, Hungary, Spain and Sweden

Healthy

15673

7962/7711

3–10

CMetS

The components used to calculate cMetS score were the same risk factors used in the adult MetS definition. cMetS score > 90th percentile was considered unfavorable

Male 26.25

Male 3–4 years

47.5

89.5

0.713 (0.622–0.804)

26.60

4–5 years

58.5

86.4

0.805 (0.740–0.871)

27.10

5–6 years

82.0

78.6

0.874 (0.820–0.928)

27.60

6–7 years

83.2

79.9

0.895 (0.856–0.934)

28.30

7–8 years

79.6

80.3

0.885 (0.848–0.922)

28.65

8–9 years

88.1

78.0

0.907 (0.872–0.942)

30.90

9–10 years

71.4

88.1

0.881 (0.772–0.991)

Female 24.95

Female 3–4 years

63.6

78.6

0.741 (0.662–0.820)

25.15

4–5 years

81.4

74.4

0.823 (0.764–0.883)

26.15

5–6 years

75.0

81.0

0.839 (0.772–0.906)

26.45

6–7 years

94.7

73.8

0.921 (0.884–0.958)

27.10

7–8 years

88.6

76.3

0.897 (0.862–0.933)

27.80

8–9 years

93.6

79.0

0.924 (0.898–0.950)

29.65

9–10 years

1.00

95.0

0.984 (0.955–1.000)

Pillai et al. [27]

Prospective observational cross-sectional

India

Women with PCOS

121

0/121

12–41

MetS

MetS by IDF

33.35

Female

60.3

70.7

0.7 (0.604–0.794)

MetS by ATP III

33.87

Female

73

69

0.722 (0.631–0.816)

Yan et al. [9]

Cross-sectional

China

Healthy

2092

971/1121

 > 65

MetS

The 2004 CDS criteria

38 cm in men and

Male

80

55

0.76

35 cm in women

Female

75

67

0.73

Obesity

BMI ≥ 25 kg/m2

38 cm in men and 35 cm in women

Male

87

62

NR

Female

80

74

Kurtoglu et al. [24]

Case–control

Turkey

Healthy

581

259/322

5–18

MetS

IDF criteria

36 cm in boys and 35 cm in girls

Male

61.9

85.6

0.766 (0.689–0.882)

Female

60.4

78.15

0.749 (0.683–0.808)

Cizza et al. [38]

Cross-sectional

USA

Obese

120

28/92

18–50

MetS

IDF criteria

NO 38 cm

Female

0.54

0.70

0.63

de LucenaFerretti et al. [20]

Cross-sectional

Brazil

Healthy

1667

751/916

10–17

Overw eight

WHO criteria

 > 34.25 in boys a nd  > 31.25 ingirls

Male

53.3

72.8

0.690 (0.649–0.730)

Female

61.2

83.0

0.775 (0.741–0.809)

Obesity

 > 37.95 in boys and  > 32.65 ingirls

Male

34.2

94.5

0.712 (0.654–0.770)

Female

63.8

90.9

0.815 (0.754–0.877)

Yang et al. [10]

Cross-sectional

China

Type 2 diabetic patient

3182

1294/1888

20–80

MetS

Chinese Diabetes Society criteria

NC 39 cm for men and 35 cm for women

Male

42.8

83.8

0.67 (0.63–0.70); p < 0.001

Female

60.0

66.5

0.66 (0.63–0.70); p < 0.001

Central obesity

WC ≥  85 cm for men and  ≥ 80 cm for women

NC 37 cm for men and 35 cm for women

Male

65.1

73.6

0.77 (0.72–0.82); p < 0.001

Female

64.0

75.9

0.75 (0.72–.078); p < 0.001

Overweight

BMI  ≥ 24 kg/m2

NC 38 cm for men and 35 cm for women

Male

Female

62.0 68.8

74.2 65.4

0.72 (0.69–0.75); p < 0.001

0.73 (0.70–0.75); p < 0.001

Zepeda et al. [39]

Cross-sectional

USA

Healthy

1058

561/497

6–18

High BP

Systolic and/or diastolic BP

 ≥ 95th percentile for age, sex and height

NC > 90,th

Male

Female

NR

NR

0.75 (0.70–0.81)

0.72 (0.63–0.75)

Katz et al. [40]

Cross-sectional

Canada

Healthy

1913

977/936

6–17

Overweight/obesity

BMI > 85th percentile CDC

NC > 50th percentile

Boys: 25.3–35.5 cm Girls: 24.8–30.5 cm

Total

0.970

0.500

0.884

Lou et al. [41]

Cross-sectional

China

Healthy

2847

1475/1372

7–12

Overweight/obesity

BMI ≥ 85th

Boys: 27.4–31.3 cm Girls: 26.3–31.4 cm

Male

Female

Total

0.803

0.847

NR

0.846 0.819

0.843 0.845

Selvan et al. [13]

Cross-sectional

India

Healthy

451

258/193

30–80

MetS

NCEPATP III

 > 34.9 cm for men

 > 31.25 cm for women

Male

Female

78.6

72.3

59.3

64.4

0.753 (0.694–0.813)

0.768 (0.687–0.849

Type 2 DM

NR

Male

Female

NR

NR

0.453 (0.382–0.5324)

0.439 (0.357–0.520)

Hypertension

NR

Male

Female

NR

NR

0.535 (0.465–0.606)

0.501 (0.416–0.586)

High TG

NR

Male

Female

NR

NR

0.670 (0.600–0.739)

0.546 (0.463–0.629)

Low HDL

NR

Male

Female

NR

NR

0.611 (0.537–0.685)

0.622 (0.537–0.707)

Hatipoglu et al. [25]

Case–control

Turkey

Overweight/obese children and healthy ones as control

967

475/492

6–18

Overweight and obesity

BMI ≥ 85th percentile of the BMI reference curve according to local references

Boys: pre-pubertal 29.0 and pubertal 32.5 cm

Girls: pre-pubertal 28.0 and pubertal 31.0 cm

Prepubertal male

86.36 (78.5–92.2)

82.58 (75.0–88.6)

0.889 (0.843–0.926)

Pubertal male

80.85 (71.4–88.2)

76.26 (68.3–83.1)

0.877 (0.828–0.916)

Prepubertal female

78.95 (68.1–87.5)

85.15 (76.7–91.4)

0.884 (0.828–0.927)

Pubertal female

83.33 (75.9–89.2)

81.42 (75.0–86.8)

0.896 (0.857–0.928)

Atwa et al. [42]

Cross-sectional

Egypt

Healthy

2762

1327/1435

12–15

Overweight/obesity

BMI > 85th CDC

Men: 29.3–32.3 cm Women:28.6–30.8 cm

Male

Female

Total

0.927

0.928

0.928

0.806

0.670

0.736

NR

Luo et al. [11]

Cross-sectional

China

Healthy

1943

783/1160

58 ±7

MetS

 ≥ 2 metabolic disorders

But without abdominal obesity

NC > 38.5 cm for men

NC > 34.5 cm for women

Male

Female

50.53 (45.93–55.12)

48.95 (44.37–53.54)

67.74 (62.23–72.92)

74.85 (71.43–78.07)

NR

Abdominal obesity

Visceral fatarea of ≥ 80 cm2

Male

Female

56.1

58.1

83.5

82.5

0.781

0.777

Diabetes

FPG ≥ 6.10 mmol/Land (or) a 2hPG  ≥ 7.80 mmol/L/L, and (or) previously diagnosed diabetes

Male

Female

46.33 (41.33–51.38)

43.64 (39.12–48.25)

59.79 (54.73–64.71)

71.08 (67.53–74.44)

NR

High BP

SBP ≥ 130 mmHg, and (or) DBP ≥ 85 mmHg, and (or) previously diagnosed hypertension serum TG > 1.70 mmol/L

Male

Female

48.88 (44.58–53.20)

42.94 (39.17–46.78)

68.98 (62.78–

74.71)

76.18 (72.14–79.90)

NR

High TG

Serum TG ≥ 1.70 mmol/L

Male

Female

52.82 (47.01–58.58)

50.73 (45.29–56.16)

62.66 (58.17–66.99)

71.67 (68.45–74.74)

NR

Low HDL-C

Serum HDL-C less than 1.04 mmol/L

Male

Female

51.45 (44.95–57.92)

58.56 (48.82–67.83)

60.33 (56.07–64.48)

67.59 (64.66–70.42)

 

Khalangot et al. [14]

Cross-sectional

Ukraine

Healthy (not registered as T2D patients)

196

46/150

 > 44

DM

HbAlc ≥ 6.5%

NO 36.5 cm for women and  >  38.5 cm for men

Female

72.2 (46.5–90.3)

62.3 (53.4–70.7)

0.690 (0.815–0.564)

Male

100 (54.1–100)

38.5 (23.4–55.4)

0.774 (0.682–0.866)

Kumar et al. [26]

Cross-sectional

India

Patients who attended medicine clinic in a tertiary care KMC hospital

431

250/181

Males  >  35 and female s > 40

MetS

ATP 111

Total: 36.5cms

Female: 34

Male: 37

Total

50.0

76.0

70

Female

82

32

NR

Male

68

32

NR

Gomez-Arbelaez et al. [16]

Cross-sectional

Colombia

Healthy

669

351/318

8–14

MetS

Modified NHANES

29 cm in boys and 28.5 cm in girls

Male

100

45.37

0.74 (0.68–0.78)

Female

87.50

53.61

0.73 (0.68 –0.78)

Insulin resistance

HOMA-IR22.6

30 cm in males and 29 cm in females

Male

52.54

61.19

0.54 (0.49–0.59)

Female

50.00

62.35

0.57 (0.51–0.62)

NC neck circumference, HOMA homeostatic model assessment, IR insulin resistance, HOMA-IR Homeostasis Model Assessment-Insulin Resistance, HDL-C high density lipoprotein-cholesterol, NCEP National Cholesterol Education Program criteria, cMetS continuous metabolic syndrome, CDS Chinese Diabetes Society, CDC Centers for Disease Control and Prevention, IDF International Diabetes Federation, MetS metabolic syndrome, BMI body mass index ,WHO World Health Organization, VFA visceral fat area, DM diabetes mellitus, FBS fasting blood glucose, HbAlc hemoglobin Ale, TG triglycerides, LDL low density lipoprotein, TC total cholesterol, WC waist circumference, BP blood pressure, NR not reported

The highest SE values of NC for prediction of MetS was 100 in children and 80 in adults. The maximum SP was 89.5 in children and 91 in adults. The SE values to predict overweight/obesity ranged from 34 to 97 in children, and the SP was between 50 and 94. Only 2 studies included adults [9, 10] wherein SE was between 62 and 87 in men, and 68 and 80 in women. SP was between 62 and 74 in men, and between 65 and 74 in women. In 2 studies which reported SE and SP of NC in the prediction of abdominal obesity [10, 11], SE ranged from 56.1 to 68.8 and SP ranged from 65.4 to 83.5. In 2 studies which reported SE and SP of NC for prediction of hypertension among children and adolescents, maximum SE and SP were 85 and 71 in boys and 100 and 69 in girls [12]. Only 3 studies assessed SE and SP of NC for the prediction of high TG and low HDL-C [1113] wherein the highest values of SE and SP were 62 and 71, respectively.

Among 4 studies which assessed SE and SP of NC for prediction of type 2 diabetes [1114], the maximum values of SE and SP was 80 and 67 in children [12], and 100 and 72 in adults. In studies which assessed insulin resistance [12, 15, 16], two studies reported a SE of 100 in boys, and 50 to 95 in girls. The SP was 42 to 74 in boys, and 36 to 84 in girls.

According to QUADAS-2 checklist, the study methods of all diagnostic accuracy studies met all QUADAS-2 items. However, three studies were classified as “unclear risk” in the domain of “patient selection” (third question of the first domain) [11, 15, 16]. One studies were classified as “high risk” in the first question of the first domain (random sampling method) [9]. Totally, 83.33% of the studies were considered as high quality (low risk of bias) and 91.66% were classified as low concern according to the QUADAS-2 checklist.

A-2: Association between NC and cardiometabolic risk factors

Articles which assessed association between NC and cardiometabolic risk factors were categorized into two sections: articles which assessed cardiometabolic risk factors as binary variables and reported odds ratio (OR) or relative risk (RR) in logistic regression analysis (Table 2), or articles which assessed cardiometabolic risk factors as continuous variables and reported correlation coefficient or Beta coefficient in correlation or linear regression analysis (Table 3).
Table 2

Characteristics of the included studies which assessed relationship between neck circumference and cardiometabolic risk factors

Study

Coun try

Type of study

Populati on

n

Male/fe male

Agey

Diagnostic criteria of outcome

Unit of NC

Sex group

Confounder

Outcome

Measure of effect

Measure of associati on

Quality score

Khalangot et al. [14]

Ukraine

Cross-sectional

Healthy not registere dasT2D patients

196

46/150

 > 44

HbAlc ≥ 6.5%

 

Both sexes

Gender, BMI

DM

1.43 (1.05–1.96); p = 0.024

Adjuste OR (95% CI)

8

Yan et al. [9]

China

Cross-sectional

Healthy

2092

971/1121

 > 65

According to the 2004 Chinese Diabetes Society (CDS) criteria [1]

 

Male

Female

Age

MetS

11.53 (5.57–23.87) 7.69(4.91-12.04)

Adjusted OR (95% CI) (Q4/Q1)

7

BMI ≥ 25 kg/m2

Male

Female

Obesity

26.26 (11.02–62.57)

17.16 (9.59–30.70)

Fasting TG21.7 mmol/l

Male

Female

HighTG

3.06 (2.06–4.54)

2.01 (1.59–2.56)

Blood

pressure > 140/90 mmHg or known treatment for hypertension

Male

Female

HighBP

2.41 (1.94–3.00)

4.37 (2.81–6.7)

FBS ≥ 6.1  mmol/l or known treatment for diabetes

Male

Female

High FBS

1.89 (1.53–2.34)

1.68 (1.41–2.00)

Zepeda et al. [39]

USA

Cross-sectional

Healthy

1058

561/497

6–8

Systolic and/or diastolic BP ≥  95th percentile for age, sex and height

NC > 90th percentile

Both sexes

Age, gender and height

High BP

1.59 (1.05–2.40)

Adjusted OR (95% CI

8

de LucenaFerretti et al. [20]

Brazi1

Cross-sectional

Healthy

1667

751/916

10–17

Overweight and obesity according to the definitions of WHO

 ≥ 34.25 in boys and  ≥  31.25 in girls

Both sexes

Sex, age, weight, BMI, WC, pubertal stage, SBP, DBP, % body fat

Overweight Obesity

1.70 (0.85–3.39)

AdjustedOR (95% CI

7

 ≥ 37.95 in boys and  ≥ 32.65 in girls

3.26 (1.00–10.59)

Pereira et al. [17]

Brazi1

Cross-sectional

College students

702

62.7% were women

20–24

NCEP Adult Treatment Panel III

Neck circumference  ≥ 39 cm for men and  ≥  35 cm for women

Both sexes

Sex, age, occupational situation

MetS

5.4 (1.4–22.1)

Adjusted OR (95% CI

7

Zhou et al. [18]

China

Cross sectional

Healthy

4201

2508/1693

20–85

MetS according to the IDF criteria

Increased TG: ( ≥  1.7 mmol/L)

Decreased HDL-C (≤ 1.29 mmol/L for women)

High BP:(SBP >  130 orDBP ≥ 85 mmHg)

Increased FBS ( ≥  5.60 mmol/L)

NC of  ≥ 37 cm for men and  ≥ 33 cm for women

Male

Age, BMI, WC and waist to hip ratio

MetS

1.29 (1.12–1.48)

Adjusted OR (95% CI

8

High BP

1.15 (1.01–1.32)

Increased TG

1.16 (1.02–1.33)

Increased FBS

1.26 (1.06–1.50)

Female

MetS

1.44 (1.20–1.72)

High BP

1.22 (1.03–1.46)

Increased TG

1.42 (1.18–1.71)

Increased FBS

1.32 (1.06–1.65)

Decreased HDL-C

1.29 (1.10–1.51)

Kuciene et al. [22]

Lithuania

Case–control

Case: hypertensive

Control: healthy

1947

962/985

12–15

Prehypertension: SBPorDBP ≥ 90th and  < 95th percentile

Hypertension: SBP or DBP  ≥ 95th percentile

NC at  > 90th percentile

Both sexes

Age, sex

Prehypertension

2.99 (1.88–4.77)

Adjusted OR (95% CI)

7

Hypertension

4.05 (3.03–5.41)

Prehyper tension/hypertension

3.75 (2.86–4.91)

Choet al. [19]

South Korea

Cohort

Healthy

3521

1784/1737

42–71

DM was defined based on the WHO criteria

–1st quartile: men: 35.1 cm Women: 30.7 cm

–4th quartile: Men: 40.3 cm

Women: 35.2 cm

Male

Female

Age, BMI orWC, family history of DM, antihypertensive medication, TG, alanine aminotransferase, hsCRP, PRA, HbAlc, HOMA-IRandlGI, daytime sleepiness

Incidence of diabetes mellitus

1.746 (1.037–2.942)

2.077 (1.068–4.038)

Adjusted

RR (95% CI)

8

Guo et al. [43]

China

Cross-sectional

Normal

6802

3631/3171

5–18

According to The Fourth Report on the Diagnosis, Evaluation, and treatment of High Blood Pressure in Children and Adolescents

NC  ≥  90th percentile

Normal weight subjects

Age, gender BMI, WC

Prehyper tension

1.439 (1.118–1.853)

Adjusted OR (95% CI)

8

Overweight subjects

1.161 (0.738–1.826)

Obese subjects

0.892 (0.429–1.854)

Vallianou et al. [44]

Greece

Cross-sectional

consecutive adults who had visited the ‘Polykliniki’ General Hospital for a health check-up

490

194/296

18–89

CRP > 0.1 mg/dL

 

Total

Age and gender years of school, smoking, physical activity status, Diet and alcohol intake

High-SE C-reactive protein

1.14 (1.05–1.23)

Adjusted OR (95% CI)

7

Kelishadi et al. [21]

Iran

Cross-sectional

School students

23043

11708/11335

6–18

Overweight was considered as BMI between the 85th and 94th centiles for age and sex, obesity as BMI  ≥  95th centile; and abdominal obesity as WHtR > 0.5.

 

Total

Adjusted for age, sex and living area

Over weight

General obesity

Abdominal obesity

1.07 (1.06–1.08)

1.10 (1.08–1.11)

1.20 (1.18–1.21)

Adjusted OR (95% CI)

7

Zen et al. [45]

Brazi1

Case–control

CHD patients

376

242/134

40 years or over

Significant coronary artery disease defined by the presence of stenosis  ≥ 50% in a major epicardial coronary artery-left anterior descendent, circumflex or right coronary artery or their branches with or at least 2.5 mm of diameter

41.6 cm in men and 37.0 cm in women

Total

Age, sex, years at school, smoking, hypertension, HDL-C and diabetes mellitus

Significant coronary stenosis

2.4 (1.1–5.3)

Adjusted OR (95% CI)

7

Table 3

Characteristics of the included studies on correlation between neck circumference and cardiometabolic risk factors

Study

Country

Type of study

Population

n

Male/femal e

Agey

Age group

Confounde r

Outcome

Measure of effect

Measure of associati on

Quality score

Kurtoglu et al. [24]

Turkey

Case–contr ol

Healthy

581

259/322

5–18

Prepubertal boys

 

BMI

r = 0.759; P <  0.001

Pearson correlatio n; P-value

7

SBP

r =  0.502; P <  0.001

DBP

r =  0.335; P <  0.001

WC

r = 0.820; P <  0.001

FBS

r = 0.172; P = 0.046

Insulin

r = 0.609; P <  0.001

TC

r = 0.302; P <  0.001

TG

r = 0.409; P <  0.001

HDL-C

r =− 0.166; P = 0.056

HOMA-IR

r = 0.619; P <  0.001

Pubertal boys

BMI

r = 0.774; P <  0.001

SBP

r = 0.452; P <  0.001

DBP

r = 0.472; P <  0.001

WC

r = 0.833; P <  0.001

FBS

r = 0.047; P = 0.650

Insulin

r = 0.325; P <  0.001

TC

r = 0.467; P <  0.001

TG

r = 0.380; P <  0.001

HDL-C

r = − 0.304; P <  0.001

HOMA-IR

r = 0.336; P = 0.001

Prepubertal girls

BMI

r = 0.783; P <  0.001

SBP

r = 0.396; P <  0.001

DBP

r = 0.317; P <  0.001

WC

r = 0.853; P <  0.001

FBS

r = 0.210; P = 0.031

Insulin

r = 0.416; P <  0.001

TC

r = 0.272; P = 0.005

TG

r = 0.208; P = 0.032

HDL-C

r = − 0.349; P <  0.001

HOMA-IR

r = 0.409; P <  0.001

Pubertal Girls

BMI

r = 0.778; P <  0.001

SBP

r = 0.268; P <  0.001

DBP

r = 0.193; P = 0.008

WC

r = 0.781; P <  0.001

FBS

r = 0.131; P = 0.074

Insulin

r = 0.455; P <  0.001

TC

r = 0.101; P = 0.170

TG

r = 0.201; P = 0.006

HDL-C

r =− 0.189; P = 0.010

HOMA-IR

r = 0.449; P <  0.001

Silva et al. [15]

Brazil

Cross-sectio nal

Healthy

388

169/219

10–19

Male

Body fat percentage and puberty

BMI Z score

0.58; P <  0.001

Adjusted Pearson correlatio n; P-value

6

WC

0.79; P <  0.001

SBP

0.47; P <  0.001

DBP

0.37; P <  0.001

       

Female

stage

FBS

− 0.08; P <  0.001

  

Fasting insulin

0.29; P <  0.001

HOMA1-IR

0.29; P <  0.001

TC

0.08

LDL-C

0.14

HDL-C

− 0.34; P <  0.001

TG

0.23; P <  0.01

BMI Z score

0.48; P <  0.001

WC

0.64; P <  0.001

SBP

0.28; P <  0.001

DBP

0.18; P <  0.01

FBS

0.08;

Fasting insulin

0.43; P <  0.001

HOMA1-IR

0.41; P <  0.001

TC

0.04;

LDL-C

0.09;

HDL-C

− 0.24; P <  0.001

TG

0.25; P <  0.001

Goncalves et al. [12]

Brazil

Cross sectio nal

Healthy

260

129/131

10–14

Total

 

Body fat

0.51; P <  0.001

Pearson correlatio n; P-value

6

WC

0.74; P <  0.001

Weight

0.75; P <  0.001

BMI

0.88; P <  0.001

Waist to height ratio

0.41; P <  0.001

WHR

0.14; P <  0.05

HOMA-IR

0.35; P <  0.001

Fasting insulin

0.36; P <  0.001

SBP

0.62; P <  0.001

DBP

0.29; P <  0.001

TC

− 0.27; P <  0.001

LDL-C

− 0.18; P <  0.05

HDL-C

− 0.27; P <  0.001

TG

0.06; P <  0.001

Gomez-Arbelaez et al. [16]

Colombia

Cross-sectio nal

Healthy

669

351/318

8–14

Total

Age, gender and Tanner stage

FBS

0.815 ±0.244; P = 0.001

Adjusted Beta ± SE

7

HDL-C

− 1.333 ± 0.384; P = 0.001

TG

3.887 ± 1.014; P <  0.001

SBP

1.719 ±0.205; P <  0.001

DBP

1.305 ±0.173;

          

P <  0.001

  

Insulin

0.362 ±0.051; P <  0.001

HOMA-IR

0.085 ±0.011; P <  0.001

Atwa et al. [42]

Egypt

Cross-sectio nal

Healthy

2762

1327/1435

12–15

Male

 

Weight

r = 0.68; P <  0.001

Pearson correlation coefficient; p-value

8

BMI

r = 0.67; P <  0.001

WC

r=0.72; P <  0.001

Female

Weight

r =  0.68; P <  0.001

BMI

r = 0.65; P <  0.001

WC

r = 0.63; P <  0.001

Pillai et al. [27]

India

Prospective observational cross-sectional

Women with PCOS

121

0/121

12–41

Female

 

WC

r = 0.758; p < 0.001

Pearson correlation coefficients

6

Vallianou et al. [44]

Greece

Cross-sectio nal

Consecutive adults who had visited the ‘Polykliniki’ GeneralHos pital for a health check-up

490

194/296

18–89

 

Age, gender, years of school, smoking, physical activity, diet, alcohol intake

SBP

0.97 (0.41–1.54); p =  0.001

Adjusted Beta (95% CI)

7

DBP

0.66 (0.31–1.01); P <  0.0001

FBS

0.003 (0.001–0.005); p =  0.003

HDL-C

_1.37 (_1.77–0.97); p <  0.0001

LDL-C

1.15 (_0.05–2.34); p = 0.06

TC

1.01 (_0.33–2.35); p = 0.14

TG

0.02 (0.01–0.03); p <  0.0001

Zepeda et al. [39]

USA

Cross-sectional

Healthy

1058

561/497

6–18

Male

 

WC

r =  0.78; P <  0.001

Pearson correlation coefficients; p-value

8

BMI

r = 0.72; P <  0.001

SBP

r =  0.44; P <  0.001

DBP

r = 0.23; P <  0.001

WHtR

r = 0.25; P <  0.001

Female

WC

r = 0.83; P <  0.001

BMI

r = 0.71; P <  0.001

SBP

r = 0.41; P <  0.001

DBP

r = 0.28; P <  0.001

WHtR

r =  0.49; P <  0.001

Luo et al. [11]

China

Cross-

Healthy

1943

783/1160

58 ±7

Male

Several

Trunk FM

0.444; P <  0.001

Adjusted

8

  

sectional

     

metabolic and body fat parameter s

visceral fat area

0.138; P <  0.001

Beta; p-value

 

Subcutaneous fat area

0.208; P <  0.001

SBP

0.052; P =  0.039

Female

Trunk FM

0.519; P <  0.001

visceral fat area

0.144; P <  0.001

Subcutaneous fat area

0.053; P =  0.032

SBP

0.098; P <  0.001

Lou et al. [41]

China

Cross-sectional

Healthy

2847

1475/1372

7–12

Male

 

Weight

r =  0.841; P <  0.001

Pearson correlation coefficient; p-value

8

BMI

r =  0.800; P <  0.001

WC

r =  0.809; P <  0.001

Female

Weight

r =  0.785; P <  0.001

BMI

r =  0.736; P <  0.001

WC

r =  0.739; P <  0.001

Selvan et al. [13]

India

Cross-sectio nal

Healthy

451

258/193

30–80

Male

Age

WC

r =  0.742; P <  0.001

Adjusted Pearson correlation coefficient; p-value

BMI

r =  0.744; P <  0.001

SBP

r =  0.106

DBP

r =  0.113

FBS

r =  0.025

TC

r =  0211; P <  0.05

TG

r =  0.365; P <  0.001

LDL-C

r =  0.185

HDL-C

r =  = − 0.319; P <  0.01

Female

WC

r =  0.713; P <  0.001

BMI

r =  0.682; P <  0.01

SBP

r =  0.172

DBP

r =  0.028

FBS

r =  0.221

TC

r= 0.003

TG

r =  0.112

LDL-C

r =  =  0.092

HDL-C

r = -0.327; P <  0.01

Katz et al. [40]

Canada

Cross-sectional

Healthy

1913

977/936

6–17

Healthy-weight male

Age

BMI

0.75 (0.62–0.88)

Adjusted Beta (95% CI)

8

Overweight/ob ese male

BMI

0.46 (0.38–0.54)

Healthy-weight male

WC

0.24 (0.18–0.3)

Overweight/ob ese male

WC

0.16 (0.13–0.18)

Healthy-weight

BMI

0.42 (0.37–0.47)

       

female

     

Overweight/ob ese female

BMI

0.37 (0.26–0.48)

Healthy-weight female

WC

0.15 (0.12–0.17)

Overweight/ob ese female

WC

0.15 (0.13–0.17)

Formisano et al. [23]

Italy, Belgium, Cyprus, Estonia, Germany, Hungary, Spain and Sweden

Cross-sectional

Healthy

15673

7962/7711

3–10

Boys

BMI z-score and country of origin

WC z-score

0.318; P <  0.001

Adjusted Pearson correlation coefficient; p-value

8

SBPz-score

0.030

DBP z-score

− 0.017

HDL-C z-score

− 0.060; P <  0.001

TG z-score

0.056; P <  0.001

HOMA index z-score

0.068; P <  0.001

Girls

WC z-score

0.357; P <  0.001

SBPz-score

0.050; P <  0.005

DBP z-score

− 0.011

HDL-C z-score

− 0.056; P <  0.005

TG z-score

0.063; P <  0.001

HOMA index z-score

0.111; P <  0.001

Cizza et al. [38]

USA

Cross-sectional

Obese

120

28/92

18–50

Total

 

MetS score

r =  0.458; p <  0.001

Pearson correlation coefficient; p-value

6

Fasting insulin

r =  0.476; P  <  0.001

HOMA index

r =  0.461; P <  0.001

Visceral fat

r =  0.674, P <  0.001

Subcutaneous fat

r =  0.125, P  =  0.20

Total

abdominal

fat%

r =  0.482, P <  0.001

Yang et al. [10]

China

Cross-sectional

Type 2 diabetic patients

3182

1294/1888

20–80

Male Female

 

BMI

r =  0.41; P < 0.0001 r = 0.84; P < 0.0001

Pearson correlation coefficient; p-value

8

Male Female

 

WC

r =  0.47; P <  0.0001 r = 0.47; P <  0.0001

Kumar et al. [26]

India

Cross-sectional

Patients who attended medicine

431

250/181

Males  >  35 and females  > 40

Total

 

BMI

0.492; P  < 0.001

Pearson correlation coefficient;

7

WC

0.453; P < 0.001

Hip

0.458; P < 0.001

W/H RATIO

− 0.005; P  =  0.912

   

Clinic in a tertiary care KMC hospital

     

SBP

0.243; P  < 0.001

p-value

 

DBP

0.107; P=0.027

FBS

0.166; P < 0.001

TC

0.266; P < 0.001

LDL

0.344; P < 0.001

HDL

− 0.173; P < 0.001

TG

0.280; P  < 0.001

Rao et al. [46]

India

Cross-sectional

Patients who visited medicine OPD of a tertiary care teaching hospital

250

180/70

40–100

Total

 

SBP

0.194056; P  =  0.002

Pearson correlation coefficient; p-value

6

DBP

0.176716; P =  0.005

Li et al. [47]

China

Cross sectional

Patients who took lower abdomen and neck CT examination s

177

87/90

35–75

Men

Women

Age

Visceral adipose tissue (VAT)

r  =  0.49, p < 0.00 r  =  0.25, p  =  0.012

Adjusted Pearson correlation coefficient; p-value

6

Men

Women

Subcutaneous adipose tissue (SAT)

r  =  0.59, p <  0.001

r  =  0.41, p <  0.001

Zhou et al. [18]

China

Cross sectional

from the Examination Centre

4201

2508/1693

20–85

Male

Age

SBP

r =  0.250; p <  0.01

Adjusted Pearson correlation coefficient; p-value

8

DBP

r =  0.261; p <  0.01

FBG

r =  0.177; p <  0.01

TG

r =  0.240; p <  0.01

HDL-C

r =  − 0.202; p<0.01

TC

r = 0.143; p <  0.01

LDL-C

r =  0.088; p <  0.01

Female

SBP

r =  0.255; p <  0.01

DBP

r =  0.189; p <  0.01

FBG

r= 0.180; p <  0.01

TG

r =  0.199; p <  0.01

HDL-C

r =  − 0.234; p<0.01

TC

r =  0.039

LDL-C

r =  0.075; p <  0.01

Saka et al. [48]

Turkey

Cross-sectional

Healthy

411

174/237

20–60

Men

Women

 

Body weight

r = 0.576; p=0.000

Pearson correlation coefficient

7

r = 0.702; p = 0.000

Men

Women

WC

r = 0.593; p = 0.000

r = 0.667; p = 0.000

       

*Men

Women

 

Hip circumferences

r = 0.568; p=0.000 r = 0.617; p = 0.000

  

Men Women

BMI

r=0.58; p = 0.000 r = 0.688; p = 0.000

Androutsos et al. [28]

Greece.

Cross-sectional

Healthy

324

167/157

9–13

Total

Age, gender, Tanner stage, physical activity, and protein-, carbohydrate- and fat-dietary intake

TC

− 0.200 ± 0.777

Adjusted (β ± SE)

7

HDL

− 1.713 ± 0.376

LDL

1.016 ±0.669

Fasting glucose

0.285 ±0.217

SBP

2.082 ±0.273

DBP

0.465 ±0.234

TG

0.037 ±0.009

Insulin

0.064 ±0.014

HOMA-IR

0.067 ±0.014

Male

 

TC

r = − 0.11

Pearson’s correlation coefficient

HDL

r = − 0.32, p<0.001

LDL

r = 0.04

FBS

r=0.10

SBP

r = 0.43, p < 0.001

DBP

r = 0.02

TG

r = 0.12

Insulin

r=0.23, p < 0.001

HOMA-IR

r = 0.23, p < 0.001

Female

TC

r = − 0.11

HDL

r = − 0.23, p<0.001

LDL

r = 0.05

FBS

r = 0.11

SBP

r=0.43, p < 0.001

DBP

r = 0.20,p < 0.05

TG

r=0.22, p < 0.05

Insulin

r = 0.35, p < 0.001

HOMA-IR

r = 0.36, p < 0.001

Total

TC

r =  = − 0.10

HDL

r = − 0.27, p<0.001

LDL

r =  = 0.01

FBS

r = 0.11

SBP

r = 0.43, p < 0.001

DBP

r = 0.09

TG

r = 0.15, p < 0.001

         

Insulin

r = 0.26, p < 0.001

  

HOMA-IR

r = 0.26, p < 0.001

Joshipura et al. [49]

San Juan, USA

Cross-sectional

Overweight/ obese, nondiabetic Hispanics

1206

54.6% male

40–65

Total

Age, gender, smoking status, physical activity

BMI

R = 0.66; p < 0.001

Adjusted Pearson correlation coefficient; p-value

8

WC

R = 0.64; p < 0.001

% body fat

R = 0.45; p < 0.001

HOMA-IR

R = 0.45; p < 0.05

FBS

R = 0.10; p < 0.001

HbAlc

R = 0.28; p < 0.001

SBP

R = 0.18; p < 0.001

HDL-C

R = − 0.23; p<0.001

DBP

R = 0.23;p < 0.001

TG

R = 0.12; p < 0.05

Hs-CRP

R = 0.30; p <0.001

Hassan et al. [29]

Egypt

Cross-sectional case control

50 healthy, 50 obese children

100

52/48

7–12

Metabolic subjects

 

Weight

0.631;P = 0.001

Pearson correlation coefficient; p-value

6

BMI

0.239; P = 0.240

WC

0.465; P = 0.017

Waist/Hip

− 0.113; P = 0.582

SBP

0.289; P = 0.152

DBP

0.445; P = 0.023

LDL

0.122; P = 0.551

HDL

− 0.120; P = 0.559

TC

0.056;P =  0.787

TG

− 0.253; P = 0.212

FBS

− 0.377; P = 0.058

Fasting Insulin

0.219; P = 0.283

HOMA-IR

0.113;P =  0.583

Non metabolic subjects

Weight

0.619; P =  0.001

BMI

0.535;P =  0.007

WC

0.605; P =  0.002

Waist/Hip

− 0.203; P =  0.340

SBP

0.048; P =  0.823

DBP

0.186; P =  0.384

LDL

− 0.444; P =  0.030

HDL

− 0.139; P =  0.516

TC

− 0.221; P =  0.299

TG

0.314; P =  0.135

FBS

− 0.137; P =  0.524

Fasting Insulin

0.119; P =  0.580

 

HOMA-IR

0.116; P =  0.591

Cho et al.[19]

South

Conor

Healthy

3521

1784/1737

42–71

Male

 

SBP

0.170; P < 0.001

Pearson

8

 

Korea

t

      

DBP

0.200; P < 0.001

Correlation coefficient; p-value

 

BMI

0.801; P < 0.001

WC

0.740; P < 0.001

Body fat (%)

0.547; P < 0.001

FPG

0.159; P <  0.001

HOMA-IR

0.317; P <  0.001

TG

0.240; P <  0.001

HDL-C

− 0.246; P <  0.001

Female

SBP

0.203; P <  0.001

DBP

0.199; P <  0.001

BMI

0.744; P <  0.001

WC

0.706; P <  0.001

Body fat (%)

0.510; P <  0.001

FPG

0.122; P <  0.001

HOMA-IR

0.234; P <  0.001

TG

0.256; P <  0.001

HDL-C

− 0.223; P <  0.001

Guo et al. [43]

China

Cross-sectio nal

Normal

6802

3631/3171

5–18

Normal weight

Age, gender, BMI, WC

BMI

r = 0.226; P <  0.001

Adjusted Pearson correlation coefficient; p-value

8

WC

r = 0.339; P <  0.001

SBP

r = 0.449; P <  0.001

DBP

r = 0.328; P <  0.001

Overweight

BMI

r = 0.137; P <  0.001

WC

r = 0.348; P <  0.001

SBP

r = 0.459; P <  0.001

DBP

r = 0.344; P <  0.001

Obese

BMI

r = − 0.004;P =  0.932

WC

r = 0.635; P <  0.001

SBP

r = 0.477; P <  0.001

DBP

r = 0.325; P <  0.001

Hatipoglu et al. [25]

Turkey

Case–control

Overweight/ obese children and healthy ones as control

967

475/492

6–18

Boys prepubertal pubertal

Girls prepubertal pubertal

 

BMI

r =  0.700; P < 0.001 r =  0.821; P < 0.001

r =  0.727; P < 0.001 r =  0.848; P<0.001

Pearson correlation coefficient; p-value

8

Boys

Prepubertal

Pubertal

Girls

Prepubertal

Pubertal

WC

r =  0.733; P < 0.001 r =  0.839; P < 0.001

r =  0.776; P < 0.001 r =  0.854; P<0.001

 

Kelishadi et al. [21]

Iran

Cross-

School

23043

11708/113

6–18

Male

Age, sex

Weight

r =  0.546; p <  0.001

Adjusted

7

  

sectional

students

 

35

  

and living area

BMI

r =  0.389; p < 0.001

Pearson correlation coefficient; p-value

 

WC

r =  0.491; p <  0.001

Waist/Hip

r =  0.035; p <  0.001

Waist/Height

r =  0.156; p <  0.001

Hip

r =  0.505; p <  0.001

Female

Weight

r =  0.481; p <  0.001

BMI

r =  0.387; p <  0.001

WC

r =  0.456; p <  0.001

Waist/Hip

r = − 0.020; p <  0.001

Waist/Height

r =  0.222; p <  0.001

Hip

r =  0.464; p <  0.001

Total

Weight

r =  0.519; p <  0.001

BMI

r =  0.384; p <  0.001

WC

r =  0.479; p <  0.001

Waist/Hip

r =  0.023; p <  0.001

Waist/Height

r =  0.188; p <  0.001

Hip

r =  0.478; p <  0.001

Table 2 lists characteristics of studies reporting OR/RR of high NC and the risk of cardiometabolic risk factors (n = 13). Most of them were designed as cross-sectional (n = 10) and the rest as case–control (n = 2) or cohort (n = 1). The studies were carried out in different countries including China (n = 3), Brazil (n = 3), Greece (n = 2), and one study in Ukraine, USA, Iran, Lithuania, and South Korea. In 6 studies, children and adolescents were included, and 7 reports were on adult populations. The articles have been published between 2012 and 2017.

Three studies in adults assessed the OR of high NC in prediction of MetS presence [9, 17, 18]. Among them, Yan et al. found the strongest association between high NC and MetS in both elderly men and women, with ORs of 11.53 and 7.69, respectively [9]. The association between high NC and DM was reported in few studies [9, 14, 18, 19] where in ORs or RRs varied between 1.26 (1.06–1.50) and 2.07 (1.06–4.03).

Three studied reported the association between high NC and obesity. Among children and adolescents, ORs was between 1.07 and 1.70 for the prediction of overweight, and 1.10 to 3.25 for prediction of obesity [20, 21].Yan et al. found again a strong association between high NC and obesity among elderly men and women, with ORs of 26.26 and 17.16, respectively [9].

In two studies which assessed the association between high NC and high TG [9, 18], the ORs were between 1.16 and 3.06. In regard to high BP, Kuciene et al. [22] found that greater NC was associated with 4 times risk for hypertension. Among adults, Yan et al. [9] found OR of 2.41 and 4.37 in elderly men and women, respectively.

Table 3 shows association studies where both NC and cardiometabolic risk factors were reported continuous variables. A total of 27 studies were found (14 publications included children and adolescents, and 13 studies in adults). Most of them used correlation coefficients, and few ones used beta regression coefficients for statistical analyses. The articles were published between 2010 and 2017. The studies were carried out in different countries including China (n = 6), India (n = 4), USA (n = 3), Turkey (n = 3), Brazil (n = 2), Egypt (n = 2), Greece (n = 2), and one study in Iran, Canada, Europe, Colombia, and South Korea.

Out of 18 studies which assessed the correlation between NC and BMI, 11 articles included children and adolescents. Significant correlations were found between NC and BMI. The r ranged from 0.38 [21] to 0.88 [12] in adolescents. In adults, r ranged from 0.41 to 0.84 in men and women together.

There was a significant association between WC and NC in all 20 studies (13 reports in children and adolescents, and 8 studies in adults). The r ranged from 0.318 [23] to 0.85 [24, 25] among children and adolescents. In adults, r-values was between 0.45 [26] and 0.75 [27].

Out of 18 studies which reported the correlation between NC and blood pressure, 9 publications were on children and adolescents. A wide range of r was found; from 0.02 [28] to 0.62 [12]. In some studies, the correlation was not significant [13, 28, 29].

Weak correlations was observed between NC and FBS in 12 relevant studies, (r ranged from − 0.377 to 0.27 [29, 30]). Eleven studies also reported correlation between fasting insulin, HOMA-IR or both with NC. The r-values for these two variables were very close, ranging from 0.21 to 0.61 [24, 30].

Fourteen studies reported correlation coefficients of blood TC, TG, HDL-C, or LDL-C with NC. Findings of correlation between TC and NC was not conclusive; r-values ranged from − 0.27 [12] to 0.302 [24]. Blood TG was positively correlated with NC in all reports [r ranged from 0.06 [12] to 0.409 [24]. There was negative correlation between HDL-C and NC in all relevant publications, with r ranging from − 0.120 [29] to − 0.35 [30]. Weak and mostly not significant correlations between LDL-C and NC were observed.

According to the Newcastle–Ottawa checklist, all selected studies were categorized as high quality study and attained score ≥ 6 according to this scale. Overall, 20% of studies attained 6 scores, 38% of studies attained 7 scores and the rest got the score of 8 (Tables 2 and 3).

Results of quantitative synthesis

B-1: The diagnostic accuracy of high NC to predict MetS

The results of heterogeneity statistics about the SE of high NC to predict MetS according to sex and age groups showed sever heterogeneity in SE existed between studies in male (I2: 97.9%; Q test: 335.85, p < 0.001), female (I2: 91.1%; Q test: 112.26, p < 0.001), pediatric (I2: 91.1%; Q test: 33.75, p < 0.001), adult (I2: 96.2%; Q test: 391.78, p < 0.001), and overall population (I2: 96%; Q test: 479.02, p < 0.001). Due to sever heterogeneity between studies, the random effect meta-analysis was used and the pooled SE in male, female, pediatric, adult and overall population was estimated 69% (95% CI 56–83), 67% (95% CI 60–74), 77% (95% CI 55–99), 65% (95% CI 58–72) and 67% (95% CI 61–74), respectively (Additional file 1: Figure S1:A–D). The results of heterogeneity statistics for SP of high NC to predict MetS indicated sever heterogeneity among studies in both sexes and age groups. The random effect meta-analysis showed that the pooled SP in male, female, adult, pediatric and overall population was 64% (95% CI 52, 75), 67% (95% CI 60, 74),66% (95% CI 60, 72), 66% (95% CI 48, 84) and 66% (95% CI 60, 73), respectively (Additional file 2: Figure S2: E–H).

Publication bias: Begg᾽s test confirmed no publication bias for sensitivity (p = 0.32) and specificity (p = 0.92) of high NC for predicting MetS.

B-2: The association of NC with glycemic indices in adult populations

FBS: The pooled estimates of 4 studies (seven effect sizes) indicated that there was a significant positive correlation between NC and serum levels of FBS (CC: 0.16, 95% CI 0.13, 0.20). However, the heterogeneity was high (I2: 56.0%, p = 0.03) (Additional file 3: Figure S3:1). Subgroup analysis based on age, sex and continent are presented in Additional file 4: Table S1. After stratification by continent (Asian, Non-Asian), we found that the association between NC and FBS concentrations in Asian population (CC: 0.19, 95% CI 0.16, 0.22; I2:0, p = 0.61) was stronger than Non-Asian (CC: 0.13, 95% CI 0.10, 0.16; I2: 28.3%, p = 0.24). This parameter attenuated the heterogeneity greater than gender subgroups.

HOMA-IR: The association between NC and HOMA-IR was reported in three studies containing four effect sizes. The overall effect size showed a significant link between NC and HOMA-IR (CC: 0.38, 95% CI 0.25, 0.50) in adult population, while the heterogeneity was high (I2: 93.5%, p = 0.0001) (Additional file 3: Figure S3:2). Due to limited studies, it was not possible to perform subgroup analysis to find the reason of the heterogeneity.

B-3: The association of NC with lipid profile in adult populations

Based on the overall effect size, in subjects who had higher NC, serum levels of TC was higher than those with smaller one (CC: 0.12, 95% CI 0.05, 0.19; I2: 79.2, p = 0.001) (Additional file 3: Figure S3:3). After stratification by age, a notable reduction was observed in the heterogeneity (Additional file 4: Table S1). Besides, pooling 8 effect sizes revealed that there was a significant correlation between NC and TG concentrations (CC: 0.23, 95% CI 0.19, 0.28; I2: 76.2%, p = 0.001) (Additional file 3: Figure S3:4). However, after subgroup analysis the heterogeneity did not attenuate considerably (Additional file 4: Table S1). Meta-analysis on LDL-C concentrations also showed a positive association with NC (CC: 0.14, 95% CI 0.07, 0.22); however, the heterogeneity was high (I2: 79.2%, p = 0.001) (Additional file 3: Figure S3:5). Subgroup analysis showed that this association in men (CC: 0.13, 95% CI 0.03, 0.22; I2: 59.1%, p = 0.11) was stronger than women (CC: 0.08, 95% CI 0.03, 0.13; I2: 0%, p = 0.81).

Publication bias: Egger᾽s test showed no publication bias for FBS (p = 0.49), HOMA-IR (p = 0.57), TC (p = 0.92), TG (p = 0.93) and LDL-C (p = 0.25).

B-4: The association of NC with glycemic indices in child populations

FBS: From five studies in which the association between NC and FBS concentrations was reported, 12 effect sizes were extracted. The pooled estimates showed that children with greater NC had higher levels of FBS compared to those with smaller one (CC: 0.12, 95% CI 0.07, 0.16; I2:48.4%, p = 0.03) (Additional file 5: Figure S4:1). No severe heterogeneity was found for this association.

HOMA-IR: The correlation between NC and HOMA-IR was reported in 6 studies including 11 effect sizes. Based on findings, greater NC was correlated with higher HOMA-IR (CC: 0.27, 95% CI 0.23, 0.31). However, the heterogeneity was considerably high (I2: 93.2%, p = 0.0001) (Additional file 5: Figure S4:2).

B-5: The association of NC with lipid profile in child populations

The pooled estimates (n = 12) of five studies showed a significant positive link between NC and TC concentrations (CC: 0.07 95% CI 0.02, 0.12; I2:87.8%, p = 0.001), although it was a weak correlation (Additional file 5: Figure S4:3). Findings of six studies also revealed a significant link between NC and TG levels (CC: 0.21, 95% CI 0.17, 0.25; I2:61.2%, p = 0.001) (Additional file 5: Figure S4:4). However, no correlation was obtained between NC and LDL-C (CC: 0.01, 95% CI − 0.06, 0.07; I2:65.9%, p = 0.005) (Additional file 5: Figure S4:5). Due to limited studies on children, subgroup analyses were not possible.

Publication bias: Begg᾽s test confirmed no publication bias for FBS (p = 0.19), HOMA-IR (p = 0.38), TC (p = 0.37), TG (p = 0.58) and LDL-C (p = 0.06).

Discussion

The current systematic review and meta-analysis revealed a positive association of NC, glycemic status and lipid profile in adult and child populations. However, no correlation was observed between NC and LDL-C concentrations in children. In general, due to high heterogeneity the findings should be declared with caution. Moreover, the association between NC and other cardio-metabolic risk factors were significant in most studies. However, because of limited studies drawing a certain decision needs further studies. Although the SE and the SP of NC to predict MetS were greater than about 65% in both child and adult populations, the between-study heterogeneity was considerably high.

To the best of our knowledge, the present study is the first study that examined the association of NC and cardio-metabolic risk factors in all age ranges and determined the SE and the SP of NC to predict MetS. In the present study, subgroup analysis revealed that the link between serum levels of FBS and NC in Asian was stronger than other adult populations. Findings on children populations also showed that the link between NC and FBS was significant only in Asian populations. Additionally, in Asian children the link between insulin resistance and NC was stronger than non-Asians.

These findings showed that race can play a main role in this correlation. Besides, the correlation between NC and LDL-C levels in men was stronger than the correlation in adult women. Therefore, gender can be another factor that affects the association. Energy intake, physical activity level, and menopause status are possible factors that can affect the link. In the present study, some included studies did not control such factors and it is likely to cause bias in the findings.

Another factor in the association between NC and cardio-metabolic risk factors is likely to be study design. In the present systematic review, design in most studies was cross-section. The weakness of this kind of study is inability to clarify a cause and effect relationship. Prospective cohort studies can shed light on the type of the association.

Although prior studies introduced WC as a good predictor for cardio-metabolic risks [31, 32], it has some limitations. For instance, several sites including midway between rib cage and iliac crest, the lower border of rib cage, and iliac crest umbilicus are used for measuring WC. This resulted in different values for WC. Moreover, time of measurement, the state of expiration and fullness affect the measure [29, 33]. However, NC measurement is easy and accessible. Besides, a unit site for measurement was reported among the studies. NC is measured above the cricoid cartilage and perpendicular to the long axis of the neck [34, 35]. Due to no variation in the measurement of NC, multiple measurements are not needed to be sure about its accuracy.

Compare to BMI, NC has some strength points. NC is measured faster and does not need special tools [9]. Therefore, particularly for epidemiological assessment it seems to be a good predictor. However, due to the high heterogeneity, more studies are needed to clarify its efficacy.

In the present study, we found that the association of NC with obesity, diabetes, hypertension, and MetS were significant in most studies. However, due to limited studies we cannot draw a fix conclusion about these issues. In addition, as there has been no meta-analysis on the SE and the SP of NC as a predictor for MetS, we could not compare our results with previous findings. Based on a systematic review by Arias et al., there was a positive association between NC and adiposity parameters indirectly measured by reference methods including dual-energy x-ray absorptiometry (DXA) and computed tomography (CT) in adult population. However, they reported no study on children in this regard [50].

The mechanisms that explain the association between neck adipose tissue and cardio-metabolic risk factors are not precisely identified. It is likely that high plasma free fatty acids (FFAs) provide a ground for developing metabolic disorders [36]. Increasing in the levels of FFAs can result in oxidative stress and vascular injury [15, 36]. The main releasing rate of systemic FFA is dedicated to upper body subcutaneous fat [5, 36]. Accordingly, NC can be a suitable predictor for CVD risk factors.

The present study has two main limitations: [1] due to cross-sectional design in the most included studies a cause and effect relationship was not clarified. [2] Heterogeneity mostly remained high even after stratification by possible confounders. The main strength of the present systematic review was to determine the SE and SP of NC in adult and child populations.

Conclusion

Although the SE and the SP of NC to predict MetS were acceptable in both child and adult population, the between-study heterogeneity was considerably high. There is a positive association between NC and glycemic indices, and lipid profile in adult and pre-pubertal populations. However, no correlation was observed between NC and LDL-C concentrations in children. Due to high heterogeneity, the findings should be declared with caution. Although the association between NC and other cardio-metabolic risk factors were significant in most studies, due to limited publications in this regard more prospective cohort studies are needed to clarify these associations.

Declarations

Authors’ contributions

AAJ, NN, SD, PC, MEA, design and data gathering, SSZ, HA, MZ, AMG, MM design and revision, MQ data analysis. All authors read and approved the final manuscript.

Acknowledgements

This study was funded by Alborz University of Medical Sciences. The authors are thankful of Emam Ali clinical research development unit for their assistance.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Please contact author for data requests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The Research and Ethics council of Alborz University of Medical Sciences approved the study (Project number: 394049).

Funding

Alborz University of Medical Sciences.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis 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.

Authors’ Affiliations

(1)
Department of Nutrition, Science and Research Branch, Islamic Azad University, Tehran, Iran
(2)
Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
(3)
Development of Research & Technology Center, Deputy of Research and Technology, Ministry of Health and Medical Education, Tehran, Iran
(4)
Non-communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
(5)
Student Research Committee, Alborz University of Medical Science, Karaj, Iran
(6)
Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran
(7)
Department of Medical Emergencies, Qom University of Medical Sciences, Qom, Iran
(8)
Department of Nutrition and Dietetics, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
(9)
Department of Basic and Clinical Research, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
(10)
Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
(11)
Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

References

  1. Roth GA, Huffman MD, Moran AE, Feigin V, Mensah GA, Naghavi M, et al. Global and regional patterns in cardiovascular mortality from 1990 to 2013. Circulation. 2015;132(17):1667–78.View ArticleGoogle Scholar
  2. Handbook of Obesity (2nd Edition)-George A. Bray Claude Bouchard-0824747739-Informa Healthcare.pdf > .Google Scholar
  3. Despres JP, Lemieux I, Bergeron J, Pibarot P, Mathieu P, Larose E, et al. Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol. 2008;28(6):1039–49.View ArticleGoogle Scholar
  4. Ben-Noun L, Sohar E, Laor A. Neck circumference as a simple screening measure for identifying overweight and obese patients. Obes Res. 2001;9(8):470–7.View ArticleGoogle Scholar
  5. Hingorjo MR, Qureshi MA, Mehdi A. Neck circumference as a useful marker of obesity: a comparison with body mass index and waist circumference. J Pak Med Assoc. 2012;62(1):36–40.PubMedGoogle Scholar
  6. Ben-Noun LL, Laor A. Relationship between changes in neck circumference and changes in blood pressure. Am J Hypertens. 2004;17(5 Pt 1):409–14.View ArticleGoogle Scholar
  7. Ozkaya I, Tunckale A. Neck circumference positively related with central obesity and overweight in Turkish university students: a preliminary study. Cent Eur J Public Health. 2016;24(2):91–4.View ArticleGoogle Scholar
  8. Ma C, Wang R, Liu Y, Lu Q, Liu X, Yin F. Diagnostic performance of neck circumference to identify overweight and obesity as defined by body mass index in children and adolescents: systematic review and meta-analysis. Ann Hum Biol. 2017;44(3):223–9.View ArticleGoogle Scholar
  9. Yan Q, Sun D, Li X, Zheng Q, Li L, Gu C, et al. Neck circumference is a valuable tool for identifying metabolic syndrome and obesity in Chinese elder subjects: a community-based study. Diabetes Metab Res Rev. 2014;30(1):69–76.View ArticleGoogle Scholar
  10. Yang GR, Yuan SY, Fu HJ, Wan G, Zhu LX, Bu XL, et al. Neck circumference positively related with central obesity, overweight, and metabolic syndrome in Chinese subjects with type 2 diabetes: Beijing Community Diabetes Study 4. Diabetes Care. 2010;33(11):2465–7.View ArticleGoogle Scholar
  11. Luo Y, Ma X, Shen Y, Xu Y, Xiong Q, Zhang X, et al. Neck circumference as an effective measure for identifying cardio-metabolic syndrome: a comparison with waist circumference. Endocrine. 2017;55(3):822–30.View ArticleGoogle Scholar
  12. Gonçalves VSS, Faria ERD, Franceschini SDCC, Priore SE. Neck circumference as predictor of excess body fat and cardiovascular risk factors in adolescents. Revista de Nutrição. 2014;27(2):161–71.View ArticleGoogle Scholar
  13. Selvan C, Dutta D, Thukral A, Nargis T, Kumar M, Mukhopadhyay S, et al. Neck height ratio is an important predictor of metabolic syndrome among Asian Indians. Indian J Endocrinol Metab. 2016;20(6):831.View ArticleGoogle Scholar
  14. 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(1):12.View ArticleGoogle Scholar
  15. Silva CdCd, Zambon MP, Vasques ACJ, Rodrigues AMdB, Camilo DF, Antonio MÂR, et al. Neck circumference as a new anthropometric indicator for prediction of insulin resistance and components of metabolic syndrome in adolescents: Brazilian Metabolic Syndrome Study. Rev Paul Pediatr. 2014;32(2):221–9.View ArticleGoogle Scholar
  16. Gomez-Arbelaez D, Camacho PA, Cohen DD, Saavedra-Cortes S, Lopez-Lopez C, Lopez-Jaramillo P. Neck circumference as a predictor of metabolic syndrome, insulin resistance and low-grade systemic inflammation in children: the ACFIES study. BMC Pediatr. 2016;16(1):31.View ArticleGoogle Scholar
  17. Pereira DCR, Araújo MFMd, Freitas RWJFd, Teixeira CRdS, Zanetti ML, Damasceno MMC. Neck circumference as a potential marker of metabolic syndrome among college students. Rev Lat Am Enfermagem. 2014;22(6):973–9.View ArticleGoogle Scholar
  18. J-y Zhou, Ge H, M-f Zhu, L-j Wang, Chen L, Y-z Tan, et al. Neck circumference as an independent predictive contributor to cardio-metabolic syndrome. Cardiovasc Diabetol. 2013;12(1):76.View ArticleGoogle Scholar
  19. Cho NH, Oh TJ, Kim KM, Choi SH, Lee JH, Park KS, et al. Neck circumference and incidence of diabetes mellitus over 10 years in the Korean genome and epidemiology study (KoGES). Sci Rep. 2015;5:18565.View ArticleGoogle Scholar
  20. de Lucena Ferretti R, de Pádua Cintra I, Passos MAZ, de Moraes Ferrari GL, Fisberg M. Elevated neck circumference and associated factors in adolescents. BMC Public Health. 2015;15(1):208.View ArticleGoogle Scholar
  21. Kelishadi R, Djalalinia S, Motlagh ME, Rahimi A, Bahreynian M, Arefirad T, et al. Association of neck circumference with general and abdominal obesity in children and adolescents: the weight disorders survey of the CASPIAN-IV study. BMJ Open. 2016;6(9):e011794.View ArticleGoogle Scholar
  22. Kuciene R, Dulskiene V, Medzioniene J. Association of neck circumference and high blood pressure in children and adolescents: a case–control study. BMC Pediatr. 2015;15(1):127.View ArticleGoogle Scholar
  23. Formisano A, Bammann K, Fraterman A, Hadjigeorgiou C, Herrmann D, Iacoviello L, et al. Efficacy of neck circumference to identify metabolic syndrome in 3–10 year-old European children: results from IDEFICS study. Nutr Metab Cardiovasc Dis. 2016;26(6):510–6.View ArticleGoogle Scholar
  24. Kurtoglu S, Hatipoglu N, Mazicioglu MM, Kondolot M. Neck circumference as a novel parameter to determine metabolic risk factors in obese children. Eur J Clin Invest. 2012;42(6):623–30.View ArticleGoogle Scholar
  25. Hatipoglu N, Mazicioglu MM, Kurtoglu S, Kendirci M. Neck circumference: an additional tool of screening overweight and obesity in childhood. Eur J Pediatr. 2010;169(6):733–9.View ArticleGoogle Scholar
  26. Kumar NV, Ismail MH, Mahesha P, Girish M, Tripathy M. Neck circumference and cardio-metabolic syndrome. J Clin Diagn Res. 2014;8(7):MC23.PubMedPubMed CentralGoogle Scholar
  27. Pillai BP, Kumar H, Jayakumar RV, Alur VC, Sheejamol V. The prevalence of metabolic syndrome in polycystic ovary syndrome in a South Indian population and the use of neck circumference in defining metabolic syndrome. Int J Diabetes Dev Ctries. 2015;35(4):469–75.View ArticleGoogle Scholar
  28. Androutsos O, Grammatikaki E, Moschonis G, Roma-Giannikou E, Chrousos G, Manios Y, et al. Neck circumference: a useful screening tool of cardiovascular risk in children. Pediatr Obes. 2012;7(3):187–95.View ArticleGoogle Scholar
  29. Hassan NE, Atef A, El-Masry SA, Ibrahim A, Al-Tohamy M, Rasheed EA, et al. Is neck circumference an indicator for metabolic complication of childhood obesity? Open Access Maced J Med Sci. 2015;3(1):26.View ArticleGoogle Scholar
  30. Fitch KV, Stanley TL, Looby SE, Rope AM, Grinspoon SK. Relationship between neck circumference and cardiometabolic parameters in HIV-infected and non–HIV-infected adults. Diabetes Care. 2011;34(4):1026–31.View ArticleGoogle Scholar
  31. Savva S, Tornaritis M, Savva M, Kourides Y, Panagi A, Silikiotou N, et al. Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. Int J Obes. 2000;24(11):1453.View ArticleGoogle Scholar
  32. De Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J. 2007;28(7):850–6.View ArticleGoogle Scholar
  33. Dixon JB, O’brien PE. Neck circumference a good predictor of raised insulin and free androgen index in obese premenopausal women: changes with weight loss. Clin Endocrinol. 2002;57(6):769–78.View ArticleGoogle Scholar
  34. Hoebel S, Swanepoel M, Malan L. Examining waist and neck circumferences as screening tools for metabolic syndrome in a sub-Saharan Caucasian cohort at three year follow-up: the SABPA prospective cohort. J Endocrinol Metab Diabetes S Afr. 2014;19(3):106–12.Google Scholar
  35. Preis SR, Massaro JM, Hoffmann U, D’Agostino RB Sr, Levy D, Robins SJ, et al. Neck circumference as a novel measure of cardiometabolic risk: the Framingham Heart study. J Clin Endocrinol Metab. 2010;95(8):3701–10.View ArticleGoogle Scholar
  36. Liang J, Teng F, Liu X, Zou C, Wang Y, Dou L, et al. Synergistic effects of neck circumference and metabolic risk factors on insulin resistance: the cardiometabolic risk in Chinese (CRC) study. Diabetol Metab Syndr. 2014;6(1):116.View ArticleGoogle Scholar
  37. Torriani M, Gill CM, Daley S, Oliveira AL, Azevedo DC, Bredella MA. Compartmental neck fat accumulation and its relation to cardiovascular risk and metabolic syndrome. Am J Clin Nutr. 2014;100(5):1244–51.View ArticleGoogle Scholar
  38. Cizza G, de Jonge L, Piaggi P, Mattingly M, Zhao X, Lucassen E, et al. Neck circumference is a predictor of metabolic syndrome and obstructive sleep apnea in short-sleeping obese men and women. Metab Syndr Relat Disord. 2014;12(4):231–41.View ArticleGoogle Scholar
  39. Zepeda A, Curcio C, Nafiu O, Prasad Y. Association of neck circumference and obesity status with elevated blood pressure in children. J Hum Hypertens. 2013;28(4):263.PubMedGoogle Scholar
  40. Katz SL, Vaccani J-P, Clarke J, Hoey L, Colley RC, Barrowman NJ. Creation of a reference dataset of neck sizes in children: standardizing a potential new tool for prediction of obesity-associated diseases? BMC Pediatr. 2014;14(1):159.View ArticleGoogle Scholar
  41. Lou D-H, Yin F-Z, Wang R, Ma C-M, Liu X-L, Lu Q. Neck circumference is an accurate and simple index for evaluating overweight and obesity in Han children. Ann Hum Biol. 2012;39(2):161–5.View ArticleGoogle Scholar
  42. Atwa H, Fiala L, Handoka NM. Neck circumference as an additional tool for detecting children with high body mass index. J Am Sci. 2012;8(10):442–6.Google Scholar
  43. Guo X, Li Y, Sun G, Yang Y, Zheng L, Xingang Z, et al. Prehypertension in children and adolescents: association with body weight and neck circumference. Intern Med. 2012;51(1):23–7.View ArticleGoogle Scholar
  44. Vallianou NG, Evangelopoulos AA, Bountziouka V, Vogiatzakis ED, Bonou MS, Barbetseas J, et al. Neck circumference is correlated with triglycerides and inversely related with HDL cholesterol beyond BMI and waist circumference. Diabetes Metab Res Rev. 2013;29(1):90–7.View ArticleGoogle Scholar
  45. Zen V, Fuchs FD, Wainstein MV, Gonçalves SC, Biavatti K, Riedner CE, et al. Neck circumference and central obesity are independent predictors of coronary artery disease in patients undergoing coronary angiography. Am J Cardiovasc Dis. 2012;2(4):323.PubMedPubMed CentralGoogle Scholar
  46. Rao KSP, Dinesh PV. Association of blood pressure in adults with selected anthropometric parameters and CRP: a hospital based cross sectional study. J Evol Med Dent Sci JEMDS. 2016;5(51):3383–8.Google Scholar
  47. Li H-X, Zhang F, Zhao D, Xin Z, Guo S-Q, Wang S-M, et al. Neck circumference as a measure of neck fat and abdominal visceral fat in Chinese adults. BMC Public Health. 2014;14(1):311.View ArticleGoogle Scholar
  48. Saka M, Türker P, Ercan A, Kızıltan G, Baş M. Is neck circumference measurement an indicator for abdominal obesity? A pilot study on Turkish Adults. Afr Health Sci. 2014;14(3):570–5.View ArticleGoogle Scholar
  49. Joshipura K, Muñoz-Torres F, Vergara J, Palacios C, Pérez CM. Neck circumference may be a better alternative to standard anthropometric measures. J Diabetes Res. 2016. https://doi.org/10.1155/2016/6058916.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Arias MT, Martinez-Tellez B, Soto J, Sanchez-Delgado G. Validity of neck circumference as a marker of adiposity in children and adolescents, and in adults: a systematic review. Nutr Hosp. 2018;35(3):707–21.Google Scholar

Copyright

© The Author(s) 2018

Advertisement