Skip to main content
Erschienen in: BMC Public Health 1/2015

Open Access 01.12.2015 | Research article

Anthropometric indices as predictors of hypertension among men and women aged 40–69 years in the Korean population: the Korean Genome and Epidemiology Study

verfasst von: Joung-Won Lee, Nam-Kyoo Lim, Tae-Hwa Baek, Sung-Hee Park, Hyun-Young Park

Erschienen in: BMC Public Health | Ausgabe 1/2015

Abstract

Background

Obesity is one of the most significant risk factors for hypertension. However, there is controversy regarding which measure is the best predictor of hypertension risk. We compared body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) in subjects as predictive indicators for development of hypertension.

Methods

The data were obtained from the Korean Genome and Epidemiology Study (KoGES), a large population-based prospective cohort study. A total of 4,454 subjects (2,128 men and 2,326 women) aged 40–69 years who did not have hypertension at baseline were included in this study. Incident hypertension was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or anti-hypertensive medication use during the 4-year follow up. Receiver operating characteristic (ROC) analysis was used to compare discrimination abilities for anthropometric indices for hypertension. Hazard ratios were calculated by Cox proportional hazard model with adjustment for age, smoking status, alcohol consumption, diabetes and family history of hypertension by sexes.

Results

In men, the area under the ROC curve (AROC) was 0.62 for WC, WHR, and WHtR and 0.58 for BMI. In women, the AROCs for BMI, WC, WHR, and WHtR were 0.57, 0.66, 0.68, and 0.68, respectively. After adjustment for risk factors, a 1 standard deviation increase in BMI, WC, WHR, WHtR were significantly related to incident hypertension, respectively (hazard ratio: 1.39, 1.50, 1.40 and 1.49 in men, 1.31, 1.44, 1.35 and 1.48 in women).

Conclusions

The central obesity indices WC, WHR, and WHtR were better than BMI for the prediction of hypertension in middle-aged Korean people. WHtR facilitates prediction of incident hypertension because of the single standard regardless of sex, ethnicity, and age group. Therefore, WHtR is recommended as a screening tool for the prediction of hypertension.
Hinweise

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JWL performed the data analysis and wrote the manuscript. NKL advised statistical analyses and interpreted the data. THB contributed discussion and revising the manuscript. SHP developed the idea and participated in drafting the manuscript. HYP contributed to study design and interpreted the data. All authors read and approved the final manuscript.
Abkürzungen
BMI
Body mass index
WC
Waist circumference
WHR
Waist-to-hip ratio
WHtR
Waist-to-height ratio
KoGES
Korean Genome and Epidemiology Study
ROC
Receiver operating characteristic
AROC
Area under the ROC curve
KSSO
Korean Society for the Study of Obesity
CVD
Cardiovascular disease
SBP
Systolic blood pressure
DBP
Diastolic blood pressure
TC
Total cholesterol
TG
Triglyceride
HDL-C
High-density lipoprotein cholesterol
LDL-C
Low-density lipoprotein cholesterol
2 h-PCPG
2-h post-challenge plasma glucose
HRs
Hazard ratios
CIs
Confidence intervals
KNHANES
Korea National Health and Nutrition Examination

Background

Obesity increases the risk of hypertension, and the prevalence of obesity in middle-aged and elderly people has increased continuously [1-4]. In Korea, the prevalences of obesity among adults (age ≥30) as defined by body mass index (BMI) and waist circumference (WC) were reported to be 35.3% and 26.3%, respectively [4]. Notably, obesity can be defined by anthropometric indices, such as BMI, WC, waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR). These anthropometric indices have been frequently used in epidemiological studies as they can be determined easily and at low cost [5]. BMI is the most widely used indicator of obesity, but it does not reflect central fat distribution, whereas WC, WHR, and WHtR are used as surrogate markers for body fat centralization [5-7]. A central distribution of body fat has been shown to be strongly associated with hypertension [5,8,9]. However, controversy remains regarding the best predictor of hypertension. Obesity has been defined as a BMI ≥30 kg/m2 in Western populations and ≥25 kg/m2 in Korean and other Asian populations [10]. Asians have higher body fat levels than Western people for the same BMI and WC according to epidemiologic studies on Asian populations [11,12]. The cut-off points for abdominal obesity in Korean adults were proposed to be a WC ≥90 cm in men and ≥85 cm in women by the Korean Society for the Study of Obesity (KSSO) [13]. The cut-off points of BMI for hypertension in men vary from 22.0 kg/m2 to 25.4 kg/m2 in different Asian countries [14]. This last study suggests the importance of applying ethnically appropriate cut-off points for anthropometric indices for hypertension. Most published research on obesity indices in relation to blood pressure is based on cross-sectional studies [2,8,13-17]. Only a few prospective studies of cut-off points for anthropometric indices for cardiovascular disease (CVD) have been conducted in Korea [18,19]. The purpose of this study was to evaluate and compare the abilities of BMI, WC, WHR, and WHtR as anthropometric indices to predict incident hypertension and to assess their associations in a Korean population aged 40 to 69 years.

Methods

Study population

The Korean Genome and Epidemiology Study (KoGES) is an ongoing community-based prospective cohort study of 10,038 participants. It was started in 2001 with the support of the Korean National Institute of Health. A baseline examination was performed on randomly selected participants in 2001–2002 and biennial follow-up examinations were subsequently conducted. The initial 2- and 4-year follow-up data are available to researchers (http://​biomi.​cdc.​go.​kr), and we obtained data for all participants from the Centers for Genome Science at the National Institute of Health, Korea. At the 2- and 4-year follow-up examinations, 7,260 participants were eligible in the present study after exclusion of 2,778 subjects who refused to participate or who had died. Of those participants, 7,233 aged 40 to 69 years were selected to participate in a baseline survey. We excluded 2,224 participants with hypertension at baseline. Additionally, 434 participants with previous CVD were excluded. Finally, after those with incomplete data were excluded, 4,454 participants remained eligible for this analysis (Figure 1). The criteria for exclusion based on hypertension at baseline were systolic blood pressure (SBP) ≥140 mmHg, diastolic blood pressure (DBP) ≥90 mmHg, or anti-hypertensive medication use. The study protocol was approved by the Institutional Review Board of the Korean Centers for Disease Control and Prevention.

Measurements and surveys

Height and weight were measured (to the nearest 0.1 cm and 0.1 kg, respectively) using a digital stadiometer and scale. BMI (kg/m2) was calculated by dividing weight by height squared. WC was measured three times at the midpoint between the bottom of the ribcage and the top of the iliac crest using a fiberglass tape measure. Hip circumference was measured three times at the point of maximal protrusion of the buttocks; the mean of the three readings was considered the final hip circumference. WHR was calculated as WC divided by hip circumference and WHtR as WC divided by height. Blood pressure was measured in the sitting position after 5 min of rest using a standard mercury sphygmomanometer. Blood samples were obtained after fasting for at least 8 h. Fasting blood glucose, total cholesterol (TC), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) levels were measured in a central, certified laboratory (Seoul Clinical Laboratories, Seoul, Republic of Korea). For subjects with TG levels <400 mg/dl, low-density lipoprotein cholesterol (LDL-C) levels were estimated indirectly using the Friedewald formula [20]. The questionnaire included questions on socio-demographic information, lifestyle, personal and family medical history, smoking status, and alcohol consumption. Smoking and alcohol consumption were defined as current smoker and current drinker, respectively.

Definition of hypertension and diabetes mellitus

In the present study, patients with an SBP of ≥140 mmHg or a DPB of ≥90 mmHg, or who used anti-hypertensive medications, were defined as having hypertension. Diabetes mellitus was defined as a fasting blood glucose level of ≥126 mg/dl, a 2-h post-challenge plasma glucose (2 h-PCPG) level of ≥200 mg/dl, an HbA1c level of ≥6.5%, or use of oral hypoglycemic agents [21].

Statistical analysis

Statistical analyses were performed using the SAS software (version 9.2; SAS Institute, Cary, North Carolina) and MedCalc (MedCalc Software, Mariakerke, Belgium). Continuous variables are expressed as the means ± SD, and discrete variables are expressed as counts and proportions. For comparisons between groups, Student’s t-test was used for continuous data and the chi-square test for categorical data. Receiver operating characteristic (ROC) analysis was used to compare discrimination abilities and to determine optimal cut-off values by sexes. Sensitivity (true-positive rate) and specificity (false-negative rate) based on cut-off values for the various anthropometric measurements and the overall discriminatory power of the diagnostic test were calculated using ROC curves. The cut-off points for hypertension were estimated using the maximized Youden index by sexes. The AUC of each obesity marker was compared those of BMI using the DeLong method [22]. We calculated hazard ratios (HRs) by Cox proportional hazard model with adjustment for age, smoking status, alcohol consumption, diabetes and family history of hypertension by sexes. The adjusted HRs are presented with 95% confidence intervals (CIs). The measurement units are different among obesity measures in the present study, so we compared the HRs according to a 1 standard deviation increase in each obesity parameter and criteria for obesity [10,13,23] by sexes. P < 0.05 was considered to indicate statistical significance.

Results

Baseline characteristics of the study subjects

The baseline characteristics of the study population, stratified by sex, are shown in Table 1. The mean age of the study population was 50.33 years in men and 50.21 years in women. The mean BMI was 23.93 kg/m2 in men and 24.36 kg/m2 in women. The mean WC was 82.45 cm in men and 79.88 cm in women. The mean WHR and WHtR were 0.88 and 0.49, respectively, in men and 0.86 and 0.52, respectively, in women. The prevalence of diabetes mellitus was higher in men than in women (6.67% vs. 5.16%, P < 0.032). Approximately 71.01% of the men were drinkers and about 50.19% were current smokers. 28.50% of women were drinkers and only 3.31% were current smokers.
Table 1
Characteristics of the study population
Variables
Men (n = 2,128)
Women (n = 2,326)
P value
Age (years)
50.33 ± 8.38
50.21 ± 8.30
0.6306
BMI (kg/m2)
23.93 ± 2.86
24.36 ± 3.09
<.0001
WC (cm)
82.45 ± 7.33
79.88 ± 9.12
<.0001
WHR
0.88 ± 0.06
0.86 ± 0.08
<.0001
WHtR
0.49 ± 0.04
0.52 ± 0.06
<.0001
SBP (mmHg)
113.80 ± 10.61
111.60 ± 11.90
<.0001
DBP (mmHg)
76.77 ± 7.39
73.73 ± 8.13
<.0001
FBG (mg/dl)
86.30 ± 15.40
82.20 ± 13.68
<.0001
TC (mg/dl)
190.40 ± 34.09
186.30 ± 33.21
<.0001
LDL-C (mg/dl)
115.20 ± 31.86
113.70 ± 29.26
0.1050
HDL-C (mg/dl)
43.66 ± 9.87
46.34 ± 9.66
<.0001
TG (mg/dl)
166.30 ± 106.20
133.10 ± 72.34
<.0001
Diabetes mellitus
142 (6.67)
120 (5.16)
0.0320
Drinker
1,511 (71.01)
663 (28.50)
<.0001
Smoker
1,068 (50.19)
77 (3.31)
<.0001
BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR. Waist-to-height ratio.
SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; TC, total cholesterol.
LDL-C, low density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides.
Values are mean ± standard deviation or n (%).
P values are from t-tests or chi-square tests for analysis of variance for continuous variables and categorical variables.

Incidence of hypertension according to anthropometric index categories

During the 4-year follow up, the overall cumulative incidence of hypertension was 18.05% (384 cases, 50.20 cases/1,000 person-years) in men and 16.08% (374 cases, 43.87 cases/1,000 person-years) in women. As shown in Table 2, the number of cases and incidence rate of hypertension significantly increased with increasing anthropometric index values in both sexes.
Table 2
Number of incident hypertension, person-years of follow-up by category of each anthropometric index
Anthropometric indices
Men (n = 2,128)
Women (n = 2,326)
Quartile
N
Event
Person-years
Incidence rate/1,000 person-years
Quartile
N
Event
Person-years
Incidence rate/1,000 person-years
BMI(kg/m2)
Q1 (<21.97)
532
70
1,956.37
35.78
Q1 (<22.28)
581
77
2,152.03
35.78
 
Q2 (21.97-23.93)
532
83
1,923.07
43.16
Q2 (22.28-24.16)
582
77
2,173.86
35.42
 
Q3 (23.94-25.84)
532
107
1,906.85
56.11
Q3 (24.17-26.16)
581
92
2,129.02
43.21
 
Q4 (≥25.85)
532
124
1,863.28
66.55
Q4 (≥26.17)
582
128
2,070.47
61.82
WC(cm)
Q1 (<77.33)
528
61
1,941.20
31.42
Q1 (<73.00)
547
42
2,065.16
20.34
 
Q2 (77.33-82.49)
533
79
1,950.32
40.51
Q2 (73.00-78.99)
593
63
2,233.75
28.20
 
Q3 (82.50-87.66)
554
112
1,975.22
56.70
Q3 (79.00-86.16)
607
112
2,198.71
50.94
 
Q4 (≥87.67)
513
132
1,782.83
74.04
Q4 (≥86.17)
579
157
2,027.75
77.43
WHR
Q1 (<0.85)
534
56
1,968.62
28.45
Q1 (<0.79)
582
30
2,224.95
13.48
 
Q2 (0.85-0.87)
531
84
1,915.97
43.84
Q2 (0.79-0.84)
581
68
2,168.85
31.35
 
Q3 (0.88-0.91)
531
101
1,905.00
53.02
Q3 (0.85-0.91)
582
123
2,083.68
59.03
 
Q4 (≥0.92)
532
143
1,859.99
76.88
Q4 (≥0.92)
581
153
2,047.89
74.71
WHtR
Q1 (<0.46)
532
58
1,961.34
29.57
Q1 (<0.47)
582
36
2,212.78
16.27
 
Q2 (0.46-0.48)
532
78
1,935.73
40.29
Q2 (0.47-0.50)
581
67
2,169.60
30.88
 
Q3 (0.49-0.52)
532
109
1,907.64
57.14
Q3 (0.51-0.55)
581
109
2,109.73
51.67
 
Q4 (≥0.52)
532
139
1,844.87
75.34
Q4 (≥0.56)
582
162
2,033.27
79.67
Total
 
2,128
384
7,649.58
50.20
 
2,326
374
8,525.38
43.87
BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height-ratio.

Cut-off points for the various anthropometric indices for predicting hypertension

Table 3 shows the AROC values and cut-off points for the anthropometric indices for predicting hypertension. In men, the AROC for WC, WHR, and WHtR was 0.62 while the AROC for BMI was 0.58. In women, the AROCs for BMI, WC, WHR, and WHtR were 0.57, 0.66, 0.68, and 0.68, respectively. In both sexes, the AROC for BMI was smaller than the AROC for the central obesity indices. The AROCs for WC, WHR, and WHtR were significantly different (P < 0.01) compared to the AROC for BMI in both sexes. The optimal cut-off points for predicting hypertension using the Youden index were 23.59 kg/m2, 83.33 cm, 0.88, and 0.49 in men and 25.63 kg/m2, 80.37 cm, 0.86, and 0.51, in women for BMI, WC, WHR, and WHtR, respectively.
Table 3
The area under the ROC curve (AROC) and cut-off points for anthropometric indices to predict the hypertension
Anthropometric index
AROC (95% CI)
Cut-off point
Sensitivity (%)
Specificity (%)
Youden index (95% CI)a)
Men
     
BMI (kg/m2)
0.58 (0.56 ~ 0.60)
23.59
66.15
47.42
0.14 (0.10 ~ 0.20)
WC (cm)
0.62 (0.60 ~ 0.64)***
83.33
59.90
57.91
0.18 (0.14 ~ 0.24)
WHR
0.62 (0.60 ~ 0.64)**
0.88
67.97
49.66
0.18 (0.14 ~ 0.24)
WHtR
0.62 (0.60 ~ 0.64)***
0.49
69.27
48.91
0.18 (0.15 ~ 0.24)
Women
     
BMI (kg/m2)
0.57 (0.55 ~ 0.59)
25.63
42.78
71.21
0.14 (0.10 ~ 0.20)
WC (cm)
0.66 (0.64 ~ 0.68)***
80.37
66.04
60.45
0.26 (0.22 ~ 0.32)
WHR
0.68 (0.66 ~ 0.70)***
0.86
71.12
57.94
0.29 (0.25 ~ 0.35)
WHtR
0.68 (0.66 ~ 0.70)***
0.51
75.13
53.18
0.28 (0.24 ~ 0.33)
CI, confidence interval; BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height-ratio.
a)95% confidence intervals of Youden index were based on 10,000 bootstrap samples.
**P <0.01, ***P <0.001 vs BMI.

HRs for hypertension according to anthropometric index

Table 4 shows that the trends were similar among the HRs for each 1-unit increase in standard deviation for each obesity parameter.
Table 4
Association between various anthropometric indices and incident hypertension
Anthropometric indices
Model 1
Model 2
Model 3
HR (95% CI)
HR (95% CI)
HR (95% CI)
Men
   
BMI (kg/m2)
1.28 (1.16 ~ 1.42)
1.38 (1.24 ~ 1.53)
1.39 (1.25 ~ 1.54)
WC (cm)
1.47 (1.33 ~ 1.63)
1.50 (1.36 ~ 1.66)
1.50 (1.36 ~ 1.67)
WHR
1.44 (1.31 ~ 1.59)
1.40 (1.27 ~ 1.55)
1.40 (1.27 ~ 1.55)
WHtR
1.49 (1.35 ~ 1.65)
1.48 (1.34 ~ 1.63)
1.49 (1.35 ~ 1.65)
Women
   
BMI (kg/m2)
1.27 (1.15 ~ 1.40)
1.30 (1.18 ~ 1.43)
1.31 (1.19 ~ 1.44)
WC (cm)
1.66 (1.51 ~ 1.83)
1.43 (1.29 ~ 1.58)
1.44 (1.30 ~ 1.60)
WHR
1.67 (1.52 ~ 1.83)
1.35 (1.21 ~ 1.50)
1.35 (1.21 ~ 1.51)
WHtR
1.76 (1.60 ~ 1.94)
1.46 (1.32 ~ 1.62)
1.48 (1.33 ~ 1.64)
HR, hazard ratio; CI, confidence interval; BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height-ratio.
Model 1, unadjusted. Model 2, adjusted for age. Model 3, adjusted for age, smoking status, alcohol consumption, diabetes and family history of hypertension.
HR per 1 standard deviation (2.86 kg/m2 in men and 3.09 kg/m2 in women, 7.33 cm in men and 9.12 cm in women, 0.06 in men and 0.08 in women, 0.04 in men and 0.06 in women) increment of BMI, WC, WHR or WHtR.
The HR for BMI was lower than those for the central obesity markers WC, WHR, and WHtR in their associations with incident hypertension in both sexes.
After adjustment for age, smoking status, alcohol consumption, diabetes and family history of hypertension, anthropometric indices showed no significant associations, but an increasing trend similar to that for the unadjusted model was maintained. Table 5 shows the HRs for incident hypertension according to obesity status defined by anthropometric indices.
Table 5
Association between various anthropometric indices and incident hypertension according to the obesity status
Variables
Men (n = 2,128)
Women (n = 2,326)
Model 1
Model 2
Model 3
Model 1
Model 2
Model 3
HR (95% CI)
HR (95% CI)
HR (95% CI)
HR (95% CI)
HR (95% CI)
HR (95% CI)
BMI ≥ 25 kg/m2a)
1.54 (1.26 ~ 1.88)
1.69 (1.38 ~ 2.08)
1.67 (1.35 ~ 2.05)
1.56 (1.27 ~ 1.90)
1.54 (1.26 ~ 1.89)
1.54 (1.26 ~ 1.89)
WC ≥ 90/85 cmb)
2.12 (1.69 ~ 2.66)
2.15 (1.71 ~ 2.70)
2.14 (1.70 ~ 2.69)
2.29 (1.87 ~ 2.81)
1.68 (1.36 ~ 2.07)
1.69 (1.37 ~ 2.08)
WHR ≥ 0.9/0.85c)
1.71 (1.40 ~ 2.09)
1.63 (1.33 ~ 1.99)
1.60 (1.31 ~ 1.96)
3.24 (2.56 ~ 4.10)
2.13 (1.66 ~ 2.75)
2.13 (1.65 ~ 2.74)
WHtR ≥ 0.5
1.89 (1.55 ~ 2.32)
1.87 (1.52 ~ 2.29)
1.86 (1.51 ~ 2.28)
3.30 (2.55 ~ 4.26)
2.24 (1.71 ~ 2.93)
2.22 (1.70 ~ 2.90)
a)BMI, WHO (2000).
b)WC ≥ 90 cm for men and ≥ 85 cm for women, Korean Society for the Study of Obesity (2007).
c)WHR ≥ 0.9 for men and ≥ 0.85 for women, WHO (1999).
HR, hazard ratio; CI, confidence interval; BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio.
WHtR, waist-to-height-ratio.
Model 1, unadjusted. Model 2, adjusted for age. Model 3, adjusted for age, smoking status, alcohol consumption, diabetes and family history of hypertension.
After adjustment for risk factors, the hazard ratios for BMI, WC, WHR and WHtR were 1.39, 1.50, 1.40 and 1.49 in men, 1.31, 1.44, 1.35 and 1.48 in women, respectively. In both sexes, the HRs for hypertension according to BMI were lower than those for the anthropometric indices related to central obesity.

Discussion

We analyzed the usefulness of anthropometric indices as predictors of hypertension. In the present study, the AROC for BMI was smaller than that for WC, WHR, and WHtR, suggesting that anthropometric indices that reflect central obesity are better for predicting hypertension in both sexes. Our results are in accordance with previous comparative studies of the association between obesity measures and hypertension [8,24,25]. The HRs for BMI were also lower than those for central obesity indices, with and without adjustment, in both sexes, similar to the AROC results. The discrimination ability of WHtR was similar to that of WC and WHR in our study, however WHtR is more convenient than other anthropometric indices. The sex differences in cut-off points were smaller for WHR and WHtR than for BMI and WC. The result of WHtR was consistent with our previous study which was cross-sectional using data of the Third Korea National Health and Nutrition Examination Survey (KNHANES III) [25]. Unfortunately however, WHR was not included in our previous study. The small sex difference is based on the same standard, which is easy to memorize and consumer-friendly. Ashwell et al. suggest a public message for adults to prevent hypertension: “keep your WC below your half height” [26]. Tseng et al. assert that these characteristics of WHtR also apply to different ethnic groups, which make it convenient for international research [27]. Moreover, WHtR is useful for children, which makes it suitable for long-term follow-up over the lifetime of an individual. Ashwell et al. investigated the relationship between CVD and anthropometric indices by meta-analysis. Discrimination of hypertension using WHtR was 3–4% better than with BMI [28]. On the other hand, WC and WHR have disadvantages. These anthropometric indices do not reflect the height of the subject [7,8]. Hsieh et al. reported that in Japanese men in the third quartile of WC (84.5– < 89 cm) short individuals had a greater risk of hypertension than those who were taller [29]. Moreover, measuring hip circumference is more difficult than measuring WC, and accurately identifying the point of maximal protrusion of the buttocks in obese people is demanding [27,30]. Previous studies reported that the AROC for WHR for hypertension was the lowest among the anthropometric indices [16,27]. Above all, WHR and WC are not consumer-friendly because of the different cut-off points according to sex [8,26,27]. Considering our results and previous studies, WHtR is an affordable screening tool for predicting hypertension in Korean adults. The present study had a number of strengths. Firstly, it used a large population-based sample and a prospective cohort design. Therefore, the causality between anthropometric indices and incident hypertension is clear. Secondly, interviews were conducted by trained interviewers and anthropometric data were obtained by repeated measurement using a standard protocol. These processes may have helped to reduce bias. The fact that dietary intakes and physical activity were not considered in the analysis is a limitation of the present study. These variables were reported to be risk factors for incident hypertension in previous studies [31,32]. Another limitation in present study is that blood pressure is measured during the visit of each follow-up. Repeated blood pressure measurements during two or more visits are recommended by the Seventh Report of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) [33].

Conclusions

In conclusion, WHtR can be recommended as a useful screening tool for predicting hypertension because of its high discrimination ability. Moreover, the cut-off points for WHtR for hypertension were similar in both sexes. WHtR is user-friendly and can be converted into a public message.

Acknowledgements

This study was supported by an intramural grant of the National Institute of Health, Korea 4800-4845-302-210(2011-NG63002).
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
The Creative Commons Public Domain Dedication waiver (https://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JWL performed the data analysis and wrote the manuscript. NKL advised statistical analyses and interpreted the data. THB contributed discussion and revising the manuscript. SHP developed the idea and participated in drafting the manuscript. HYP contributed to study design and interpreted the data. All authors read and approved the final manuscript.
Literatur
1.
Zurück zum Zitat Wang W, Lee ET, Fabsitz RR, Devereux R, Best L, Welty TK, et al. A longitudinal study of hypertension risk factors and their relation to cardiovascular disease: the Strong Heart Study. Hypertension. 2006;47(3):403–9.CrossRefPubMed Wang W, Lee ET, Fabsitz RR, Devereux R, Best L, Welty TK, et al. A longitudinal study of hypertension risk factors and their relation to cardiovascular disease: the Strong Heart Study. Hypertension. 2006;47(3):403–9.CrossRefPubMed
2.
Zurück zum Zitat Tuan NT, Adair LS, Stevens J, Popkin BM. Prediction of hypertension by different anthropometric indices in adults: the change in estimate approach. Public Health Nutr. 2010;13(5):639–46.CrossRefPubMed Tuan NT, Adair LS, Stevens J, Popkin BM. Prediction of hypertension by different anthropometric indices in adults: the change in estimate approach. Public Health Nutr. 2010;13(5):639–46.CrossRefPubMed
3.
Zurück zum Zitat Panagiotakos DB, Chrysohoou C, Pitsavos C, Skoumas J, Lentzas Y, Katinioti A, et al. Hierarchical analysis of anthropometric indices in the prediction of 5-year incidence of hypertension in apparently healthy adults: the ATTICA study. Atherosclerosis. 2009;206(1):314–20.CrossRefPubMed Panagiotakos DB, Chrysohoou C, Pitsavos C, Skoumas J, Lentzas Y, Katinioti A, et al. Hierarchical analysis of anthropometric indices in the prediction of 5-year incidence of hypertension in apparently healthy adults: the ATTICA study. Atherosclerosis. 2009;206(1):314–20.CrossRefPubMed
4.
Zurück zum Zitat Korea Centers for Disease Control and Prevention. Korea Health Statistics 2012: Korea National Health and Nutrition Examination Survey (KNHANESV-3). Seoul, Korea: Korean Ministry of Health and Welfare; 2013. Korea Centers for Disease Control and Prevention. Korea Health Statistics 2012: Korea National Health and Nutrition Examination Survey (KNHANESV-3). Seoul, Korea: Korean Ministry of Health and Welfare; 2013.
5.
Zurück zum Zitat Zhou Z, Hu D, Chen J. Association between obesity indices and blood pressure or hypertension: which index is the best? Public Health Nutr. 2009;12(8):1061–71.CrossRefPubMed Zhou Z, Hu D, Chen J. Association between obesity indices and blood pressure or hypertension: which index is the best? Public Health Nutr. 2009;12(8):1061–71.CrossRefPubMed
6.
Zurück zum Zitat Ko GT, Chan JC, Woo J, Lau E, Yeung VT, Chow CC, et al. Simple anthropometric indexes and cardiovascular risk factors in Chinese. Int J Obes Relat Metab Disord. 1997;21(11):995–1001.CrossRefPubMed Ko GT, Chan JC, Woo J, Lau E, Yeung VT, Chow CC, et al. Simple anthropometric indexes and cardiovascular risk factors in Chinese. Int J Obes Relat Metab Disord. 1997;21(11):995–1001.CrossRefPubMed
7.
Zurück zum Zitat Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev. 2010;23(2):247–69.CrossRefPubMed Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev. 2010;23(2):247–69.CrossRefPubMed
8.
Zurück zum Zitat Li WC, Chen IC, Chang YC, Loke SS, Wang SH, Hsiao KY. Waist-to-height ratio, waist circumference, and body mass index as indices of cardiometabolic risk among 36,642 Taiwanese adults. Eur J Nutr. 2013;52(1):57–65.CrossRefPubMed Li WC, Chen IC, Chang YC, Loke SS, Wang SH, Hsiao KY. Waist-to-height ratio, waist circumference, and body mass index as indices of cardiometabolic risk among 36,642 Taiwanese adults. Eur J Nutr. 2013;52(1):57–65.CrossRefPubMed
9.
Zurück zum Zitat Hsieh SD, Yoshinaga H. Waist/height ratio as a simple and useful predictor of coronary heart disease risk factors in women. Intern Med (Tokyo, Japan). 1995;34(12):1147–52.CrossRef Hsieh SD, Yoshinaga H. Waist/height ratio as a simple and useful predictor of coronary heart disease risk factors in women. Intern Med (Tokyo, Japan). 1995;34(12):1147–52.CrossRef
10.
Zurück zum Zitat World health Organization Western Pacific Region (WHO-WPR). The Asia-Pacific Perspective: redefining Obesity and its treatment. Melbourne, Australia: Health Communications; 2000. World health Organization Western Pacific Region (WHO-WPR). The Asia-Pacific Perspective: redefining Obesity and its treatment. Melbourne, Australia: Health Communications; 2000.
11.
Zurück zum Zitat Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord. 1998;22(12):1164–71.CrossRefPubMed Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord. 1998;22(12):1164–71.CrossRefPubMed
12.
Zurück zum Zitat Chang CJ, Wu CH, Chang CS, Yao WJ, Yang YC, Wu JS, et al. Low body mass index but high percent body fat in Taiwanese subjects: implications of obesity cutoffs. Int J Obes Relat Metab Disord. 2003;27(2):253–9.CrossRefPubMed Chang CJ, Wu CH, Chang CS, Yao WJ, Yang YC, Wu JS, et al. Low body mass index but high percent body fat in Taiwanese subjects: implications of obesity cutoffs. Int J Obes Relat Metab Disord. 2003;27(2):253–9.CrossRefPubMed
13.
Zurück zum Zitat Lee SY, Park HS, Kim DJ, Han JH, Kim SM, Cho GJ, et al. Appropriate waist circumference cutoff points for central obesity in Korean adults. Diabetes Res Clin Pract. 2007;75(1):72–80.CrossRefPubMed Lee SY, Park HS, Kim DJ, Han JH, Kim SM, Cho GJ, et al. Appropriate waist circumference cutoff points for central obesity in Korean adults. Diabetes Res Clin Pract. 2007;75(1):72–80.CrossRefPubMed
14.
Zurück zum Zitat Zaher ZM, Zambari R, Pheng CS, Muruga V, Ng B, Appannah G, et al. Optimal cut-off levels to define obesity: body mass index and waist circumference, and their relationship to cardiovascular disease, dyslipidaemia, hypertension and diabetes in Malaysia. Asia Pac J Clin Nutr. 2009;18(2):209–16.PubMed Zaher ZM, Zambari R, Pheng CS, Muruga V, Ng B, Appannah G, et al. Optimal cut-off levels to define obesity: body mass index and waist circumference, and their relationship to cardiovascular disease, dyslipidaemia, hypertension and diabetes in Malaysia. Asia Pac J Clin Nutr. 2009;18(2):209–16.PubMed
15.
Zurück zum Zitat Dong X, Liu Y, Yang J, Sun Y, Chen L. Efficiency of anthropometric indicators of obesity for identifying cardiovascular risk factors in a Chinese population. Postgrad Med J. 2011;87(1026):251–6.CrossRefPubMed Dong X, Liu Y, Yang J, Sun Y, Chen L. Efficiency of anthropometric indicators of obesity for identifying cardiovascular risk factors in a Chinese population. Postgrad Med J. 2011;87(1026):251–6.CrossRefPubMed
16.
Zurück zum Zitat Gupta S, Kapoor S. Optimal cut-off values of anthropometric markers to predict hypertension in North Indian population. J Community Health. 2012;37(2):441–7.CrossRefPubMed Gupta S, Kapoor S. Optimal cut-off values of anthropometric markers to predict hypertension in North Indian population. J Community Health. 2012;37(2):441–7.CrossRefPubMed
17.
Zurück zum Zitat Gupta S, Kapoor S. Sex differences in blood pressure levels and its association with obesity indices: who is at greater risk. Ethn Dis. 2010;20(4):370–5.PubMed Gupta S, Kapoor S. Sex differences in blood pressure levels and its association with obesity indices: who is at greater risk. Ethn Dis. 2010;20(4):370–5.PubMed
18.
Zurück zum Zitat Baik I, Shin C. Optimal waist circumference for the prevention of cardiovascular disease. Korean J Community Nutr. 2010;15(2):275–83. Baik I, Shin C. Optimal waist circumference for the prevention of cardiovascular disease. Korean J Community Nutr. 2010;15(2):275–83.
19.
Zurück zum Zitat Choi SJ, Keam B, Park SH, Park HY. Appropriate waist circumference cut-offs to predict diabetes in the Korean population - the Korean Genome and Epidemiology Study. Circ J. 2010;74(7):1357–63.CrossRefPubMed Choi SJ, Keam B, Park SH, Park HY. Appropriate waist circumference cut-offs to predict diabetes in the Korean population - the Korean Genome and Epidemiology Study. Circ J. 2010;74(7):1357–63.CrossRefPubMed
20.
Zurück zum Zitat Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502.PubMed Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502.PubMed
21.
Zurück zum Zitat World Health Organization and International Diabetes Fedaration. Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF Consultation. Geneva, Switzerland: World Health Organization; 2006. World Health Organization and International Diabetes Fedaration. Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF Consultation. Geneva, Switzerland: World Health Organization; 2006.
22.
Zurück zum Zitat DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.CrossRefPubMed DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.CrossRefPubMed
23.
Zurück zum Zitat World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications: report of WHO consultation. Geneva, Switzerland: World Health Organization; 1999. World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications: report of WHO consultation. Geneva, Switzerland: World Health Organization; 1999.
24.
Zurück zum Zitat Hadaegh F, Zabetian A, Sarbakhsh P, Khalili D, James WP, Azizi F. Appropriate cutoff values of anthropometric variables to predict cardiovascular outcomes: 7.6 years follow-up in an Iranian population. Int J Obes (2005). 2009;33(12):1437–45.CrossRef Hadaegh F, Zabetian A, Sarbakhsh P, Khalili D, James WP, Azizi F. Appropriate cutoff values of anthropometric variables to predict cardiovascular outcomes: 7.6 years follow-up in an Iranian population. Int J Obes (2005). 2009;33(12):1437–45.CrossRef
25.
Zurück zum Zitat Park SH, Choi SJ, Lee KS, Park HY. Waist circumference and waist-to-height ratio as predictors of cardiovascular disease risk in Korean adults. Circ J. 2009;73(9):1643–50.CrossRefPubMed Park SH, Choi SJ, Lee KS, Park HY. Waist circumference and waist-to-height ratio as predictors of cardiovascular disease risk in Korean adults. Circ J. 2009;73(9):1643–50.CrossRefPubMed
26.
Zurück zum Zitat Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56(5):303–7.CrossRefPubMed Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56(5):303–7.CrossRefPubMed
27.
Zurück zum Zitat Tseng CH, Chong CK, Chan TT, Bai CH, You SL, Chiou HY, et al. Optimal anthropometric factor cutoffs for hyperglycemia, hypertension and dyslipidemia for the Taiwanese population. Atherosclerosis. 2010;210(2):585–9.CrossRefPubMed Tseng CH, Chong CK, Chan TT, Bai CH, You SL, Chiou HY, et al. Optimal anthropometric factor cutoffs for hyperglycemia, hypertension and dyslipidemia for the Taiwanese population. Atherosclerosis. 2010;210(2):585–9.CrossRefPubMed
28.
Zurück zum Zitat Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275–86.CrossRefPubMed Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275–86.CrossRefPubMed
29.
Zurück zum Zitat Hsieh SD, Yoshinaga H. Do people with similar waist circumference share similar health risks irrespective of height? Tohoku J Exp Med. 1999;188(1):55–60.CrossRefPubMed Hsieh SD, Yoshinaga H. Do people with similar waist circumference share similar health risks irrespective of height? Tohoku J Exp Med. 1999;188(1):55–60.CrossRefPubMed
30.
Zurück zum Zitat World Health Organization. Waist circumference and waist–hip ratio: report of WHO expert consultation. Geneva, Switzerland: World Health Organization; 2008. World Health Organization. Waist circumference and waist–hip ratio: report of WHO expert consultation. Geneva, Switzerland: World Health Organization; 2008.
31.
Zurück zum Zitat Lim NK, Son KH, Lee KS, Park HY, Cho MC. Predicting the risk of incident hypertension in a Korean middle-aged population: Korean genome and epidemiology study. J Clin Hypertens (Greenwich, Conn). 2013;15(5):344–9.CrossRef Lim NK, Son KH, Lee KS, Park HY, Cho MC. Predicting the risk of incident hypertension in a Korean middle-aged population: Korean genome and epidemiology study. J Clin Hypertens (Greenwich, Conn). 2013;15(5):344–9.CrossRef
32.
Zurück zum Zitat Dyer AR, Elliott P, Shipley M, Stamler R, Stamler J. Body mass index and associations of sodium and potassium with blood pressure in INTERSALT. Hypertension. 1994;23(6 Pt 1):729–36.CrossRefPubMed Dyer AR, Elliott P, Shipley M, Stamler R, Stamler J. Body mass index and associations of sodium and potassium with blood pressure in INTERSALT. Hypertension. 1994;23(6 Pt 1):729–36.CrossRefPubMed
33.
Zurück zum Zitat Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–72.CrossRefPubMed Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–72.CrossRefPubMed
Metadaten
Titel
Anthropometric indices as predictors of hypertension among men and women aged 40–69 years in the Korean population: the Korean Genome and Epidemiology Study
verfasst von
Joung-Won Lee
Nam-Kyoo Lim
Tae-Hwa Baek
Sung-Hee Park
Hyun-Young Park
Publikationsdatum
01.12.2015
Verlag
BioMed Central
Erschienen in
BMC Public Health / Ausgabe 1/2015
Elektronische ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-015-1471-5

Weitere Artikel der Ausgabe 1/2015

BMC Public Health 1/2015 Zur Ausgabe