# Neck Circumference as a Predictive Indicator of CKD for High Cardiovascular Risk Patients

Authors: Ya-Fang Liu; Shih-Tai Chang; Wei-Shiang Lin; Jen-Te Hsu; Chang-Min Chung; Jung-Jung Chang; Kuo-Chun Hung; Kang-Hua Chen; Chi-Wen Chang; Fu-Chi Chen; Yun-Wen Shih; Chi-Ming Chu
Journal: BioMed Research International
 (2015)

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## Abstract

Background. Neck circumference (NC) is an anthropometric measure of obesity for upper subcutaneous adipose tissue distribution which is associated with cardiometabolic risk. This study investigated whether NC is associated with indicators of chronic kidney disease (CKD) for high cardiometabolic risk patients. Methods. A total of 177 consecutive patients who underwent the outpatient departments of cardiology were prospectively enrolled in the study. The patients were aged >20 years with normal renal function or with stages 1–4 CKD. A linear regression was performed using the Enter method to present an unadjusted R2, standardized coefficients, and standard error, and the Durbin-Watson test was used to assess residual independence. Results. Most anthropometric measurements from patients aged ≧65 were lower than those from patients aged <65, except for women’s waist circumference (WC) and waist hip ratio. Female NC obtained the highest R2 values for 24 hr CCR, uric acid, microalbuminuria, hsCRP, triglycerides, and HDL compared to BMI, WC, and hip circumference. The significances of female NC with 24 hr CCR and uric acid were improved after adjusted age and serum creatinine. Conclusions. NC is associated with indicators of CKD for high cardiometabolic risk patients and can be routinely measured as easy as WC in the future.


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## Body

## 1. Introduction

Cardiovascular disease (CVD) and chronic kidney disease (CKD) are two important global public health concerns that share common cardiometabolic risk factors that include hypertension, diabetes mellitus, and hyperlipidemia. The above three diseases generally coexist, making them the most common risk factors for CVD [1]. In 2007, the Taiwan Health Promotion Administration of the Ministry of Health and Welfare conducted a survey involving 5,895 healthy individuals in Taiwan and concluded that, among those patients with hypertension, diabetes mellitus, and hyperlipidemia, their rates of developing heart disease in 5 years are 1.9, 1.5, and 1.8 times greater than those without hypertension, diabetes mellitus, and hyperlipidemia, respectively [2]. Cardiovascular events include fatal and nonfatal cardiovascular events (stroke, infarction, angina, and heart failure). Hypertension, diabetes mellitus, and hyperlipidemia contribute to the increased risk of experiencing cardiovascular events [3]. It is suggested that patients who currently have CVD or have received coronary revascularization should take steps to mitigate their cardiovascular risks [4].Hypertension, diabetes mellitus, and hyperlipidemia are also initiation factors for CKD [5]. One study showed that mild renal insufficiency was common among community-based citizens and that this condition was related to the high prevalence of CVD. Mild renal insufficiency increases the risk of cardiovascular events and total mortality [6]. It was reported that a lower estimated glomerular filtration rate (eGFR) correlates with a higher risk of cardiovascular events and the related death [7]. Microvascular abnormality is a common triggering factor for both CVD and CKD [8]. The Kidney Early Evaluation Program study suggested that conditions for cardiovascular risk are defined as a urine albumin: creatinine ratio ≥30 mg/g (3.4 mg/mmol) or an eGFR < 60 mL/min/1.73 m2 [7].The main complication of CKD is CVD. Observations made in the Kidney Early Evaluation Program and the National Health and Nutrition Examination Survey studies suggested that CKD may increase the prevalence of myocardial infarction or stroke [8]. Medium to severe renal function impairments exacerbate atherosclerosis, which in turn leads to an increased risk of cardiovascular events [9]. This means the deaths of CKD patients are more likely caused by CVD than by progression of kidney failure [10]. However, patients with CVD also suffer from a higher risk of CKD [11]. Patients who have undergone coronary revascularization must be aware of their potential CKD risk for the future. That CKD may also cause atherosclerosis in coronary arteries and the cerebrovascular and peripheral circulatory system [12]. Additionally, the coexistence of CVD and CKD leads to higher mortality [6]. Therefore, CVD and CKD should be considered mutual risk factors for each other. Among CVD patients the CKD prevention is necessary, and similar condition is conversely alike. Therefore, development of an indicator that predicts the risk of both conditions would help to effectively treat the coexistence of both diseases.Abdominal obesity and general obesity would cause increases in triglycerides, low density lipoprotein (LDL) cholesterol, blood sugar, insulin, and blood pressures. They will also cause decreases in high density lipoprotein (HDL) cholesterol. All of these factors may lead to onset of CVD, which in turn increases coronary heart disease onset or mortality and total mortality [13, 14]. Neck circumference (NC) is an indicator of upper body fat distribution, whereas body morphologies and fat distribution can be used as factors to assess the risk of obesity [15]. In addition to body mass index (BMI), waist circumference, and hip circumference measurements as indicators of obesity, NC can also be used as a novel indicator for cardiometabolic risk [16, 17]. However, previous studies have not focused on NC with CKD. This pilot study aimed to investigate the relationship between NC and CKD. We intend to confirm whether NC is as an associated indicator of CKD for high cardiovascular risk patients or whether it is as an alternative option for disabled or bedbound patients for whom other body anthropometric indicator measurements are not suitable.


## 2. Materials and Methods

### 2.1. Research Framework and Participants

This study, as given in Figure1, involved 177 patients with cardiovascular risk. Inclusion criteria were patients aged >20 years with normal renal function or with stages 1~4 CKD, excluding conditions such as acute renal failure, inherited kidney diseases, nephritic syndrome, cancer, and long-term use of steroid. This study was approved by the Institute Review Committees at Tri-Service General Hospital and Chiayi Chang Gung Memorial Hospital and all the participants signed informed consent prior to study enrolment.Figure 1
Research framework and participants.


### 2.2. Data Collection

Patients’ demographic characteristics were obtained via face-interviewed questionnaires. Blood pressure and anthropometric measures, such as height, weight, NC, waist circumference, and hip circumference, were measured. Laboratory parameters were assessed for urine and fasting blood samples, which were collected in the same day. Creatinine concentration was determined using the Modified Jaffe Method with a SYNCHRON LXI 725 (Tokyo, Japan) instrument; cystatin C concentration was determined using a particle-enhanced turbid metric immunoassay with a Hitachi 7170 (Tokyo, Japan) instrument.


### 2.3. Assessment of Renal Function

GFR was calculated based on the 24-hour urine creatinine clearance rate (24 hr CCR) to assess renal function. The equation for 24 hr CCR is as follows: (24 hr urine total volume × urine creatinine concentration)/(creatinine concentration × 1440). The eGFR calculation was based on Modification of Diet in Renal Disease (MDRD), as eGFRMDRD = 186 × crea−1.154 × age−0.203 × (0.742 if female) × (1.212 if black), and Cockcroft-Gault Equation (CG), as eGFRCG = [(140 − age) × weight (kg)] ÷ (creatinine (mg/dL) × 72) × (0.85 if female).


### 2.4. Data Processing

All data were grouped by gender or age. At-test was performed to assess the differences between ages within the same gender groups. The relevance between NC and each laboratory measurement was assessed using the Spearman correlation analysis. Continuous data for males and females were analyzed separately using the Kolmogorov-Smirnov test or the Shapiro-Wilk test for normal distribution. If data were not normally distributed, logarithmic transformation of the data was performed to make the data fit a normal distribution. To investigate the relevance between anthropometric measures and heart disease and CKD risk factors, a linear regression was performed using the Enter method to present an unadjusted R2, standardized coefficients, and standard error, and the Durbin-Watson test was used to assess residual independence. An age adjustment was made for all assessed cardiovascular risk factors. To avoid the impact of renal function differences in individuals, age and log(creatinine) were included to correct the risk factors assessment. All statistical analyses were performed using SPSS 18.0 (Statistical Package for Social Sciences, Chicago, IL, USA). A P value < 0.05 was considered a statistically significant level.


## 2.1. Research Framework and Participants

This study, as given in Figure1, involved 177 patients with cardiovascular risk. Inclusion criteria were patients aged >20 years with normal renal function or with stages 1~4 CKD, excluding conditions such as acute renal failure, inherited kidney diseases, nephritic syndrome, cancer, and long-term use of steroid. This study was approved by the Institute Review Committees at Tri-Service General Hospital and Chiayi Chang Gung Memorial Hospital and all the participants signed informed consent prior to study enrolment.Figure 1
Research framework and participants.


## 2.2. Data Collection

Patients’ demographic characteristics were obtained via face-interviewed questionnaires. Blood pressure and anthropometric measures, such as height, weight, NC, waist circumference, and hip circumference, were measured. Laboratory parameters were assessed for urine and fasting blood samples, which were collected in the same day. Creatinine concentration was determined using the Modified Jaffe Method with a SYNCHRON LXI 725 (Tokyo, Japan) instrument; cystatin C concentration was determined using a particle-enhanced turbid metric immunoassay with a Hitachi 7170 (Tokyo, Japan) instrument.


## 2.3. Assessment of Renal Function

GFR was calculated based on the 24-hour urine creatinine clearance rate (24 hr CCR) to assess renal function. The equation for 24 hr CCR is as follows: (24 hr urine total volume × urine creatinine concentration)/(creatinine concentration × 1440). The eGFR calculation was based on Modification of Diet in Renal Disease (MDRD), as eGFRMDRD = 186 × crea−1.154 × age−0.203 × (0.742 if female) × (1.212 if black), and Cockcroft-Gault Equation (CG), as eGFRCG = [(140 − age) × weight (kg)] ÷ (creatinine (mg/dL) × 72) × (0.85 if female).


## 2.4. Data Processing

All data were grouped by gender or age. At-test was performed to assess the differences between ages within the same gender groups. The relevance between NC and each laboratory measurement was assessed using the Spearman correlation analysis. Continuous data for males and females were analyzed separately using the Kolmogorov-Smirnov test or the Shapiro-Wilk test for normal distribution. If data were not normally distributed, logarithmic transformation of the data was performed to make the data fit a normal distribution. To investigate the relevance between anthropometric measures and heart disease and CKD risk factors, a linear regression was performed using the Enter method to present an unadjusted R2, standardized coefficients, and standard error, and the Durbin-Watson test was used to assess residual independence. An age adjustment was made for all assessed cardiovascular risk factors. To avoid the impact of renal function differences in individuals, age and log(creatinine) were included to correct the risk factors assessment. All statistical analyses were performed using SPSS 18.0 (Statistical Package for Social Sciences, Chicago, IL, USA). A P value < 0.05 was considered a statistically significant level.


## 3. Results

The characteristics of 177 participants in this study are presented in Table1. There were 122 (71%) males with a mean age of 66 and 50 (29%) females with a mean age of 67. For most anthropometric measures, the measurements from patients ≧ 65 years old were lower than those from patients < 65 years old, except for women’s waist circumference and waist hip ratio. Differences were observed in weight, BMI, diastolic blood pressure, triglycerides, serum creatinine, cystatin C, phosphorus, 24 hr CCR, eGFRMDRD, and eGFRCG between different ages among males. In each age of females, the differences were observed in height, weight, BMI, hip circumference, waist hip ratio, NC, cystatin C, albumin, urine creatinine, 24 hr CCR, and eGFRCG. Elderly patients of both genders typically perform a poor renal function.Table 1
Study characteristics by sex and age.
Variables, mean (SD)
Men,n = 125
Women,n = 52
<65 yrs (n = 57)
≧65 yrs (n = 68)
P value
<65 yrs (n = 26)
≧65 yrs (n = 26)
P value
Age (yrs)
58 (5.2)
73.7 (5.3)
<0.001
58.6 (5.5)
75.1 (6)
<0.001
Height (cm)
166.2 (6.1)
164.8 (4.8)
0.166
157 (5.7)
153 (5.8)
0.015
Weight (kg)
76.8 (11.8)
70.8 (10.4)
0.003
68.9 (12)
59.7 (8.6)
0.002
Body mass index (kg/m2)
27.7 (3.5)
26 (3.7)
0.011
27.9 (4)
25.5 (3)
0.017
Waist circumference (cm)
97.3 (9.7)
94.5 (9.3)
0.104
92.3 (11.3)
95.8 (9.2)
0.230
Hip circumference (cm)
101.8 (7.2)
100.2 (7.2)
0.224
102.3 (10.4)
97.5 (5.9)
0.049
Waist hip ratio
1 (0.1)
0.9 (0.1)
0.198
0.9 (0.1)
1 (0.1)
0.001
Neck circumference (cm)
40.6 (3.3)
39.6 (3.3)
0.100
36.6 (2.9)
34.7 (2.6)
0.017
Systolic blood pressure (mmHg)
137.3 (12.3)
136.4 (16.1)
0.732
132.1 (12)
133.9 (11.7)
0.588
Diastolic blood pressure (mmHg)
79.9 (6.7)
76.8 (8.9)
0.030
78 (8)
73.9 (8.3)
0.073
Fasting glucose (mg/dL)
119.5 (44.2)
110.9 (27)
0.205
111.3 (30.5)
112.5 (27.6)
0.883
Total cholesterol (mg/dL)
183.2 (34.5)
181.2 (32.2)
0.748
205.6 (41.1)
197.8 (29.4)
0.448
LDL cholesterol (mg/dL)
114.5 (29.9)
114.5 (27.1)
0.990
123.5 (37.1)
112.9 (27.4)
0.258
HDL cholesterol (mg/dL)
44.3 (7.8)
46.5 (15.4)
0.315
53.9 (12.9)
49.9 (11.3)
0.258
Triglyceride (mg/dL)
148.5 (74.4)
117.1 (46.9)
0.006
137.9 (80.1)
136.3 (53.4)
0.937
Serum creatinine (mg/dL)
1.1 (0.3)
1.3 (0.5)
0.011
0.9 (0.2)
1 (0.4)
0.584
Cystatin C (mg/L)
0.9 (0.2)
1.2 (0.4)
<0.001
0.9 (0.2)
1.1 (0.3)
0.024
Blood urea nitrogen (mg/dL)
17.9 (6.3)
20.2 (7.2)
0.075
16.9 (4.8)
18.9 (10.1)
0.373
Uric acid (mg/dL)
7 (1.6)
6.9 (1.4)
0.908
6 (1.6)
6.2 (1.5)
0.632
Albumin (mg/dL)
4.4 (0.5)
4.4 (0.3)
0.437
4.4 (0.3)
4.3 (0.2)
0.046
hsCRP (mg/L)
2 (2)
1.6 (2.2)
0.319
2.1 (2.5)
2.1 (2.7)
0.992
Microalbuminuria (mg/day)
23.4 (33)
29.5 (61.1)
0.505
29.2 (54.8)
17.6 (34.6)
0.377
Total protein (mg/dL)
7.4 (0.5)
7.6 (0.5)
0.094
7.7 (0.5)
7.5 (0.4)
0.138
Aspartate aminotransferase (U/L)
28.9 (23.1)
30.7 (30.2)
0.721
25.4 (10.6)
26.3 (20.3)
0.842
Alanine aminotransferase (U/L)
35.7 (31.2)
33.8 (37.4)
0.753
35.7 (30.9)
23 (18.7)
0.085
Alkaline phosphatase (U/L)
63.6 (24.4)
61.4 (15.1)
0.777
65.7 (15.1)
64 (33.1)
0.912
Calcium (mg/dL)
8.8 (0.5)
8.7 (0.5)
0.655
8.7 (0.5)
8.8 (0.7)
0.547
Phosphorus (mmol/L)
3.5 (0.4)
3.2 (0.5)
0.012
3.8 (0.3)
3.7 (0.5)
0.175
Sodium (mmol/L)
143.7 (2)
143.2 (2.2)
0.200
144.4 (2)
143.8 (2)
0.299
Potassium (mEq/L)
4.9 (5.3)
4.9 (5.4)
0.953
5.8 (8.1)
6.4 (9.1)
0.842
Bilirubin (mg/dL)
1.1 (1.4)
0.9 (0.5)
0.233
0.8 (0.2)
0.8 (0.3)
0.194
Urine creatinine (mg/dL)
74.6 (28.7)
79.7 (36.2)
0.395
71.9 (30.9)
56.1 (17.9)
0.034
24 hr CCR (mL/min)
103.3 (33.1)
78.1 (31.7)
<0.001
93 (33.3)
67.8 (25.9)
0.004
eGFRMDRD (mL/min/1.73 m2)
75.1 (18.9)
62.1 (17.5)
<0.001
72.6 (20)
67 (19)
0.319
eGFRCG (mL/min/1.73 m2)
81.7 (23.6)
53.7 (16.5)
<0.001
76.8 (23.8)
52 (15.2)
<0.001Table2 showed the Spearman correlation coefficients with NC between both sexes. In males, most relevant coefficients were significant, especially those anthropometric measures, fasting glucose, triglycerides, cystatin C, aspartate aminotransferase, 24 hr CCR, and eGFRCG, where positive correlations were observed between NC and 24 hr CCR, eGFRCG, hsCRP, triglycerides, and LDL cholesterol. In females, diastolic blood pressure, triglycerides, uric acid, albumin, high-sensitivity C-reactive protein (hsCRP), total protein, phosphorus, and eGFRCG reached significance, where negative correlations were observed between NC and total cholesterol, HDL, total protein, aspartate aminotransferase, alanine aminotransferase, phosphorus, sodium, and potassium.Table 2
Spearman correlation coefficients of neck circumference by sex and age.
Variables
Men < 65 yrs (n = 57)
Men ≧ 65 yrs (n = 68)
Women < 65 yrs (n = 26)
Women ≧ 65 yrs (n = 26)
Coefficients
P value
Coefficients
P value
Coefficients
P value
Coefficients
P value
Age (yrs)
−0.263
0.048
−0.113
0.361
−0.373
0.060
0.167
0.414
Height (cm)
0.125
0.355
0.149
0.224
0.552
0.003
0.255
0.209
Weight (kg)
0.677
<0.001
0.649
<0.001
0.635
<0.001
0.760
<0.001
Body mass index (kg/m2)
0.738
<0.001
0.643
<0.001
0.417
0.034
0.668
<0.001
Waist circumference (cm)
0.665
<0.001
0.712
<0.001
0.331
0.098
0.622
0.001
Hip circumference (cm)
0.660
<0.001
0.743
<0.001
0.560
0.003
0.832
<0.001
Waist hip ratio
0.342
0.009
0.337
0.005
−0.054
0.794
0.114
0.579
Systolic blood pressure (mmHg)
0.059
0.665
0.230
0.059
0.159
0.438
0.185
0.365
Diastolic blood pressure (mmHg)
0.141
0.295
0.229
0.060
0.648
<0.001
−0.170
0.406
Fasting glucose (mg/dL)
0.125
0.356
0.339
0.006
−0.027
0.899
0.092
0.662
Total cholesterol (mg/dL)
0.222
0.097
0.104
0.409
−0.170
0.418
−0.032
0.879
LDL cholesterol (mg/dL)
0.227
0.089
0.223
0.074
−0.229
0.271
0.193
0.356
HDL cholesterol (mg/dL)
0.099
0.464
−0.181
0.156
−0.274
0.185
−0.261
0.218
Triglyceride (mg/dL)
0.176
0.191
0.339
0.006
0.295
0.152
0.423
0.044
Creatinine (mg/dL)
−0.167
0.215
−0.076
0.546
0.057
0.788
−0.036
0.866
Cystatin C (mg/L)
0.303
0.034
−0.100
0.450
0.276
0.226
−0.041
0.863
Blood urea nitrogen (mg/dL)
−0.040
0.773
0.116
0.377
−0.090
0.668
0.318
0.121
Uric acid (mg/dL)
−0.202
0.135
0.113
0.374
0.630
0.001
0.140
0.505
Albumin (mg/dL)
0.057
0.674
0.082
0.521
0.268
0.195
−0.397
0.050
hsCRP (mg/L)
0.188
0.174
0.244
0.058
0.627
0.001
0.053
0.806
Microalbuminuria (mg/day)
0.238
0.077
0.049
0.709
0.380
0.067
0.256
0.217
Total protein (mg/dL)
0.044
0.745
0.078
0.541
−0.040
0.849
−0.607
0.001
Aspartate aminotransferase (U/L)
0.003
0.982
−0.314
0.012
−0.011
0.957
−0.161
0.443
Alanine aminotransferase (U/L)
0.028
0.834
0.089
0.482
−0.020
0.925
−0.044
0.833
Alkaline phosphatase (U/L)
0.443
0.172
0.185
0.526
−0.319
0.538
0.559
0.192
Calcium (mg/dL)
−0.018
0.895
0.157
0.215
0.260
0.209
0.171
0.414
Phosphorus (mmol/L)
−0.121
0.373
−0.013
0.918
−0.221
0.288
−0.432
0.031
Sodium (mmol/L)
0.070
0.606
0.117
0.357
−0.134
0.524
−0.237
0.255
Potassium (mEq/L)
0.001
0.995
0.261
0.067
−0.055
0.818
−0.026
0.915
Bilirubin (mg/dL)
0.001
0.997
−0.041
0.746
0.277
0.180
−0.116
0.581
Urine creatinine (mg/dL)
0.165
0.220
0.037
0.770
0.249
0.230
−0.011
0.958
24 hr CCR (mL/min)
0.440
0.001
0.296
0.017
0.231
0.267
0.064
0.760
eGFRMDRD (mL/min/1.73 m2)
0.216
0.107
0.077
0.541
−0.066
0.752
0.039
0.852
eGFRCG (mL/min/1.73 m2)
0.475
<0.001
0.355
0.004
0.341
0.095
0.442
0.027Regression curves after age adjustment are shown in Table3(a). Compared to BMI, waist circumference, and hip circumference, the higher R2 values were obtained for female NC and microalbuminuria, hsCRP, triglycerides, and HDL cholesterol. Age- and log(creatinine)-adjusted curves are shown in Table 3(b). The items showing statistical significance in male and female NC are roughly the same as those in Table 3(a). Female NC was adjusted for age and log(creatinine), the significance of 24 hr CCR and uric acid was improved. Compared to BMI, waist circumference, and hip circumference, the highest R2 values were obtained for female NC and 24 hr CCR, uric acid, microalbuminuria, hsCRP, triglycerides, and HDL cholesterol (negative correlation).Table 3
(a) Age adjusted linear regression of CVD and CKD risk among body shape indicators. (b) Age- and log(creatinine)-adjusted linear regression of CVD and CKD risk among body shape indicators.
(a)
Dependent variables
Men (n=125)
Women (n=52)
Independent variables
R
2
B (SE)
P value
R
2
B (SE)
P value
24 hr CCR
log⁡(Neck circumference)
0.279
269.32 (76.68)
0.001
0.232
114.479 (122.29)
0.243
log⁡(Body mass index)
0.290
181.14 (47.90)
<0.001
0.211
−26.755 (73.81)
0.719
log⁡(Waist circumference)
0.310
268.82 (62.92)
<0.001
0.210
−13.095 (87.87)
0.882
log⁡(Hip circumference)
0.337
412.97 (84.67)
<0.001
0.210
−30.973 (117.5)
0.793
eGFRCG
log⁡(Neck circumference)
0.524
206.02 (44.09)
<0.001
0.503
163.779 (71.69)
0.027
log⁡(Body mass index)
0.591
172.49 (25.72)
<0.001
0.534
121.746 (41.36)
0.005
log⁡(Waist circumference)
0.601
237.27 (33.85)
<0.001
0.531
142.509 (49.32)
0.006
log⁡(Hip circumference)
0.588
312.44 (47.22)
<0.001
0.576
236.87 (62.72)
<0.001
eGFRMDRD
log⁡(Neck circumference)
0.140
20.87 (46.60)
0.655
0.081
−50.119 (81.36)
0.541
log⁡(Body mass index)
0.144
25.1 (29.26)
0.393
0.130
−82.537 (47.14)
0.086
log⁡(Waist circumference)
0.150
49.83 (38.86)
0.202
0.084
−42.682 (57.52)
0.462
log⁡(Hip circumference)
0.157
85.06 (53.13)
0.112
0.096
−82.797 (76.46)
0.284
log
⁡(Cystatin C)
log⁡(Neck circumference)
0.179
0.43 (0.31)
0.166
0.210
0.131 (0.49)
0.789
log⁡(Body mass index)
0.166
−0.09 (0.19)
0.624
0.313
0.635 (0.26)
0.021
log⁡(Waist circumference)
0.166
0.15 (0.26)
0.571
0.263
0.543 (0.33)
0.104
log⁡(Hip circumference)
0.169
0.29 (0.36)
0.419
0.254
0.634 (0.42)
0.139
Uric Acid
log⁡(Neck circumference)
0.009
−3.75 (3.99)
0.349
0.188
19.258 (6.05)
0.003
log⁡(Body mass index)
0.002
0.02 (2.45)
0.993
0.216
12.354 (3.54)
0.001
log⁡(Waist circumference)
0.002
−0.41 (3.28)
0.901
0.111
10.223 (4.48)
0.027
log⁡(Hip circumference)
0.014
−5.39 (4.48)
0.231
0.149
16.098 (5.87)
0.009
log
⁡(Urine microalbuminuria)
log⁡(Neck circumference)
0.021
2.36 (1.51)
0.120
0.200
7.255 (2.16)
0.002
log⁡(Body mass index)
0.093
3.14 (0.92)
0.001
0.105
3.089 (1.35)
0.027
log⁡(Waist circumference)
0.058
3.26 (1.23)
0.009
0.110
3.764 (1.6)
0.023
log⁡(Hip circumference)
0.049
4.09 (1.70)
0.017
0.06
3.661 (2.19)
0.102
log
⁡(hsCRP)
log⁡(Neck circumference)
0.048
2.98 (1.40)
0.036
0.101
4.366 (1.96)
0.031
log⁡(Body mass index)
0.062
2.13 (0.85)
0.014
0.067
2.091 (1.18)
0.084
log⁡(Waist circumference)
0.055
2.66 (1.14)
0.022
0.063
2.407 (1.41)
0.094
log⁡(Hip circumference)
0.043
3.15 (1.59)
0.049
0.095
3.995 (1.86)
0.037
log
⁡(Systolic blood pressure)
log⁡(Neck circumference)
0.015
0.14 (0.12)
0.252
0.025
0.175 (0.16)
0.292
log⁡(Body mass index)
0.042
0.16 (0.07)
0.031
0.003
−0.006 (0.1)
0.951
log⁡(Waist circumference)
0.031
0.18 (0.10)
0.068
0.006
0.049 (0.12)
0.674
log⁡(Hip circumference)
0.036
0.27 (0.14)
0.047
0.006
0.061 (0.16)
0.698
Diastolic blood pressure
log⁡(Neck circumference)
0.105
27.44 (19.94)
0.171
0.260
31.523 (30.9)
0.313
log⁡(Body mass index)
0.157
37.4 (12.09)
0.002
0.045
−4.045 (18.69)
0.830
log⁡(Waist circumference)
0.114
29.63 (16.58)
0.076
0.273
29.645 (21.42)
0.173
log⁡(Hip circumference)
0.123
48.09 (22.66)
0.036
0.260
29.157 (29.33)
0.325
log
⁡(Fasting plasma glucose)
log⁡(Neck circumference)
0.023
0.48 (0.29)
0.100
0.013
0.142 (0.41)
0.731
log⁡(Body mass index)
0.055
0.47 (0.18)
0.010
0.022
0.176 (0.24)
0.474
log⁡(Waist circumference)
0.094
0.82 (0.23)
0.001
0.017
0.165 (0.29)
0.573
log⁡(Hip circumference)
0.034
0.68 (0.33)
0.042
0.016
−0.19 (0.39)
0.627
log
⁡(Triglyceride)
log⁡(Neck circumference)
0.137
1.49 (0.46)
0.002
0.172
2.45 (0.82)
0.005
log⁡(Body mass index)
0.089
0.56 (0.30)
0.064
0.145
1.379 (0.51)
0.010
log⁡(Waist circumference)
0.087
0.73 (0.40)
0.070
0.138
1.562 (0.6)
0.012
log⁡(Hip circumference)
0.094
1.12 (0.55)
0.042
0.094
1.691 (0.82)
0.044
Total cholesterol
log⁡(Neck circumference)
0.023
134.99 (85.59)
0.117
0.007
−69.724 (153.93)
0.653
log⁡(Body mass index)
0.015
66.37 (54.09)
0.222
0.004
23.785 (91.82)
0.797
log⁡(Waist circumference)
0.012
76.58 (72.20)
0.291
0.006
46.529 (109.06)
0.672
log⁡(Hip circumference)
0.007
71.37 (99.35)
0.474
0.003
30.962 (146.12)
0.833
LDL cholesterol
log⁡(Neck circumference)
0.040
158.88 (72.59)
0.031
0.012
27.354 (141.34)
0.847
log⁡(Body mass index)
0.048
109.69 (45.5)
0.017
0.030
80.653 (83.39)
0.338
log⁡(Waist circumference)
0.023
99.41 (61.43)
0.108
0.017
51.923 (99.87)
0.606
log⁡(Hip circumference)
0.009
84.18 (84.89)
0.323
0.016
66.655 (133.64)
0.620
log
⁡(HDL cholesterol)
log⁡(Neck circumference)
0.008
−0.19 (0.29)
0.506
0.101
−0.936 (0.42)
0.032
log⁡(Body mass index)
0.032
−0.33 (0.18)
0.071
0.023
−0.241 (0.26)
0.365
log⁡(Waist circumference)
0.011
−0.22 (0.24)
0.376
0.024
−0.294 (0.31)
0.354
log⁡(Hip circumference)
0.005
−0.14 (0.34)
0.688
0.017
−0.311 (0.43)
0.471
R
2: unadjusted R2; B: unstandardized coefficients beta; SE: unstandardized coefficients Std. Error.
(b)
Dependent variables
Men (n=125)
Women (n=52)
Independent variables
R
2
B (SE)
P value
R
2
B (SE)
P value
24 hr CCR
log⁡(Neck circumference)
0.642
260.86 (54.29)
<0.001
0.340
169.5 (114.94)
0.147
log⁡(Body mass index)
0.621
138.8 (35.39)
<0.001
0.310
22.78 (72.38)
0.754
log⁡(Waist circumference)
0.643
221.38 (45.7)
<0.001
0.310
17.76 (83.87)
0.833
log⁡(Hip circumference)
0.659
337.81 (61.38)
<0.001
0.309
7.43 (112.11)
0.947
eGFRCG
log⁡(Neck circumference)
0.867
200.2 (23.38)
<0.001
0.800
193.98 (46.15)
<0.001
log⁡(Body mass index)
0.891
143.99 (13.43)
<0.001
0.924
193.43 (17.47)
<0.001
log⁡(Waist circumference)
0.907
205.1 (16.53)
<0.001
0.857
183.11 (27.77)
<0.001
log⁡(Hip circumference)
0.889
261.02 (24.76)
<0.001
0.910
288.32 (29.52)
<0.001
eGFRMDRD
log⁡(Neck circumference)
0.923
13.96 (14.05)
0.322
0.945
−7.1 (20.16)
0.726
log⁡(Body mass index)
0.923
−11.03 (8.88)
0.217
0.945
3.87 (12.42)
0.757
log⁡(Waist circumference)
0.922
9.6 (11.86)
0.420
0.946
12.38 (14.28)
0.391
log⁡(Hip circumference)
0.923
20.57 (16.24)
0.208
0.946
−14.26 (19.12)
0.460
log
⁡(Cystatin C)
log⁡(Neck circumference)
0.674
0.33 (0.2)
0.098
0.619
0.14 (0.34)
0.681
log⁡(Body mass index)
0.665
0.02 (0.12)
0.852
0.639
0.3 (0.2)
0.141
log⁡(Waist circumference)
0.670
0.21 (0.17)
0.215
0.637
0.33 (0.23)
0.169
log⁡(Hip circumference)
0.679
0.47 (0.22)
0.037
0.631
0.36 (0.3)
0.242
Uric Acid
log⁡(Neck circumference)
0.138
−3.49 (3.74)
0.353
0.346
17.8 (5.5)
0.002
log⁡(Body mass index)
0.133
1.17 (2.3)
0.612
0.319
9.93 (3.46)
0.006
log⁡(Waist circumference)
0.132
0.87 (3.08)
0.778
0.262
8.4 (4.17)
0.050
log⁡(Hip circumference)
0.136
−3.34 (4.24)
0.432
0.296
13.84 (5.44)
0.014
log
⁡(Urine microalbuminuria)
log⁡(Neck circumference)
0.096
2.62 (1.46)
0.075
0.271
6.68 (2.1)
0.003
log⁡(Body mass index)
0.168
3.24 (0.89)
<0.001
0.168
2.47 (1.36)
0.077
log⁡(Waist circumference)
0.142
3.64 (1.18)
0.003
0.191
3.35 (1.56)
0.037
log⁡(Hip circumference)
0.134
4.74 (1.64)
0.005
0.145
3.02 (2.14)
0.164
log
⁡(hsCRP)
log⁡(Neck circumference)
0.073
3 (1.39)
0.033
0.114
4.25 (1.97)
0.037
log⁡(Body mass index)
0.094
2.3 (0.85)
0.008
0.072
1.92 (1.24)
0.127
log⁡(Waist circumference)
0.085
2.85 (1.13)
0.014
0.074
2.26 (1.43)
0.121
log⁡(Hip circumference)
0.073
3.44 (1.58)
0.031
0.104
3.82 (1.88)
0.048
log
⁡(Systolic blood pressure)
log⁡(Neck circumference)
0.017
0.14 (0.12)
0.248
0.028
0.14 (0.17)
0.388
log⁡(Body mass index)
0.045
0.16 (0.07)
0.030
0.012
0.01 (0.1)
0.927
log⁡(Waist circumference)
0.034
0.19 (0.1)
0.066
0.022
0.08 (0.12)
0.494
log⁡(Hip circumference)
0.037
0.27 (0.14)
0.053
0.017
0.08 (0.16)
0.633
Diastolic blood pressure
log⁡(Neck circumference)
0.127
27.95 (19.83)
0.161
0.211
29.04 (29.34)
0.328
log⁡(Body mass index)
0.169
35.02 (12.26)
0.005
0.197
8.09 (18.23)
0.659
log⁡(Waist circumference)
0.132
27.86 (16.66)
0.097
0.227
28.83 (20.72)
0.171
log⁡(Hip circumference)
0.139
43.71 (22.83)
0.058
0.240
45.54 (27.46)
0.104
log
⁡(Fasting plasma glucose)
log⁡(Neck circumference)
0.033
0.47 (0.29)
0.103
0.068
0.09 (0.41)
0.826
log⁡(Body mass index)
0.061
0.45 (0.18)
0.014
0.068
0.08 (0.25)
0.765
log⁡(Waist circumference)
0.100
0.8 (0.23)
0.001
0.069
0.1 (0.29)
0.733
log⁡(Hip circumference)
0.041
0.64 (0.33)
0.055
0.077
−0.28 (0.38)
0.469
log
⁡(Triglyceride)
log⁡(Neck circumference)
0.138
1.49 (0.46)
0.002
0.436
2.25 (0.69)
0.002
log⁡(Body mass index)
0.090
0.57 (0.3)
0.061
0.355
0.92 (0.47)
0.056
log⁡(Waist circumference)
0.088
0.74 (0.4)
0.068
0.384
1.27 (0.52)
0.018
log⁡(Hip circumference)
0.095
1.14 (0.55)
0.040
0.351
1.32 (0.7)
0.067
Total cholesterol
log⁡(Neck circumference)
0.033
133.61 (85.5)
0.121
0.007
−71.5 (156.05)
0.649
log⁡(Body mass index)
0.023
59.89 (54.46)
0.274
0.004
22.75 (96.28)
0.814
log⁡(Waist circumference)
0.021
69.07 (72.53)
0.343
0.006
45.77 (111.37)
0.683
log⁡(Hip circumference)
0.016
59 (99.97)
0.556
0.003
29.37 (149.02)
0.845
LDL cholesterol
log⁡(Neck circumference)
0.048
157.85 (72.6)
0.032
0.012
28.43 (143.31)
0.844
log⁡(Body mass index)
0.052
105.66 (45.9)
0.023
0.033
88.74 (87.34)
0.315
log⁡(Waist circumference)
0.029
94.18 (61.81)
0.130
0.017
54.16 (101.96)
0.598
log⁡(Hip circumference)
0.016
75.3 (85.54)
0.381
0.017
69.33 (136.28)
0.613
log
⁡(HDL cholesterol)
log⁡(Neck circumference)
0.023
−0.2 (0.29)
0.494
0.202
−0.86 (0.4)
0.039
log⁡(Body mass index)
0.051
−0.36 (0.18)
0.049
0.123
−0.07 (0.26)
0.782
log⁡(Waist circumference)
0.028
−0.25 (0.24)
0.305
0.128
−0.18 (0.3)
0.562
log⁡(Hip circumference)
0.022
−0.18 (0.34)
0.596
0.124
−0.15 (0.41)
0.724
R
2: unadjusted R2; B: unstandardized coefficients beta; SE: unstandardized coefficients Std. Error.


## 4. Discussion

Several studies have revealed the relationship between body adipose abnormalities, CVD, and metabolic syndrome [16–29] as well as between cardiometabolic factors and CKD [1, 4, 5, 7–11, 30, 31]. However, there is lack of reporting on the relevance between NC and CKD. This is the first study to report their relationship and it is discovered that NC is associated with indicators of renal disease such as 24 hr CCR, eGFRCG, uric acid, and urine microalbuminuria, in addition to conventional cardiovascular risk factors such as hsCRP, triglycerides, LDL cholesterol, and HDL cholesterol.NC is an alternative measurement for upper-body subcutaneous fat, and therefore NC may play a vital role in CVD clinical prediction [16]. NC as an associated factor for diabetes, after adjusted BMI and waist circumference, was the only risk factor related to type II diabetes mellitus [26]. These results confirmed that NC measurements can be used as an effective clinical screening tool for insulin resistance, and can be used as powerful indicators to improve the screening ability for type II diabetes mellitus. In addition to insulin resistance and type II diabetes mellitus, NC also has associated power to assess cardiometabolic risk [28].NC also correlated significantly with intima-media thickness of common or internal carotid arteries after BMI and waist circumference adjustment. For every 1-standard deviation unit increase in NC, there is a 0.025 mm thickness increase in common carotid artery, and remaining significant even after BMI adjustment [27]. In a follow-up study involving acute ischemic stroke patients who have a 1-year total mortality of 8.9%, the author discovered that aging and larger neck circumference were more frequent findings among the dead, but not obesity. Therefore, NC is a critical clinical warning factor for fatal outcomes in acute ischemic stroke [25]. Insulin resistance, related with NC, causes arterial stiffness, which in turn has an impact on CKD and even nondiabetic CKD. CKD patients who develop metabolic syndrome would also have a higher risk of arterial stiffness [30].Although gender differences of anthropometric measures, systolic blood pressure, total cholesterol, LDL cholesterol, HDL cholesterol, and fasting plasma glucose exist, NC is also related to cardiometabolic risk [16] and is a powerful indicator for predicting dyslipidemia. In particular, the correlation between triglycerides and NC is stronger than BMI and waist circumference [28]. Moreover, the relevance and importance of waist circumference and metabolic syndrome to CVD have been discussed previously [32]. One study showed that dual indicators using both triglycerides and waist circumference can assess the risk of coronary artery disease in patients with abdominal obesity. When the Framingham model was used for predicting CVD risk, these dual indicators may help to add associated value. The hypertriglyceridemic-waist phenotype might become a superior health indicator compared to metabolic syndrome [33]. Another study in CKD patients suggested that the hypertriglyceridemic-waist phenotype can be used to evaluate the severity of carotid atherosclerosis and can also be used as an effective indicator to predict CVD risk [31]. Our study showed NC is highly correlated to waist circumference and triglycerides. Therefore, NC may replace waist circumference for phenotyping, especially because this phenotype can be used for both coronary heart disease and CKD. NC is an ideal associated indicator for these two coexisting diseases that are risk factors for each other. Moreover, for patients who are disabled, in serious condition, in particular those who are bedridden, or even in healthy status, this method would be not only simple but also easy to perform with high efficiency and convenience. Therefore, NC may be popularized for clinical applications as an associated indicator of risk for both outcomes.Based on the eGFRCG equation, we know that weight and eGFR are positively correlated. Specifically, heavier weights correlate with higher eGFRCG among those with the same sex, age, and creatinine level. From Table 2 in this study, we can conclude that NC and weight are positively correlated. Subjects aged <65 years have higher mean NC values and also have higher mean eGFRCG values and eGFRMDRD, indicating that patients with higher NC values usually have better renal function. This means that NC is positively correlated with eGFR. The eGFRMDRD and eGFRCG equations had the gender-specific coefficient of creatinine.Hoebel et al. have established the cutoffs for NC to evaluate the risk of metabolic syndrome, which were 40 cm and 41 cm for the young and older men, 34 cm and 33 cm for young and older women, respectively [22], which indicates potential uses of NC for other purposes. Body morphologies, other than NC, have an existing normal level; out of which indicate unhealthy conditions. Appropriate cut-off values for NC should be established, in the future, to assess early stage risk of CKD. This cross-sectional study relied on hospital-based samples. It is suggested that a future population-based study and a cohort-based study with follow-up should be conducted, so that the importance of NC to the CKD risk prediction can be validated, and it can be determined whether this importance will change with increasing age. However, in fashion experience and healthy population, the circumference of someone’s neck would be about the same as his/her pants width; that is, the waist circumference is about twice the neck circumference [34]. Several studies report similar ratios around 1.9 to 2.1 for circumferences of waist to neck in control groups [16, 17, 21, 22, 29].Many indicators are testing for obesity and NC is an anthropometric measure of obesity for upper subcutaneous adipose tissue distribution which is associated with cardiometabolic risk, while NC is an alternative and novel indicator to test body fat distribution [17], and it is also a simple and time-saving screening tool to capture overweight or obese patient [20]. NC also is an easy associated tool for metabolic syndrome and insulin resistance [22] and a powerful indicator of atherosclerotic lipid abnormalities and their risk factors [17]. NC is also an independent associated factor for cardiometabolic risk [16, 29] and an effective screening test for cardiovascular risk in children [18] or even in obese children [23]. Studies mentioned above have suggested that NC is a superior indicator for metabolic syndrome in terms of both relevance and significance compared to BMI and waist circumference [17, 19, 21, 24, 28, 35, 36].


## 5. Conclusions

NC is associated with indicators of CKD for high cardiometabolic risk patients and can be routinely measured as easy as WC in the future.



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*Source: 745410-2015-07-29.xml*