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J Obes Metab Syndr 2018;27(3):158-165 https://doi.org/10.7570/jomes.2018.27.3.158
Association of Z-Score of the Log-Transformed A Body Shape Index with Cardiovascular Disease in People Who Are Obese but Metabolically Healthy: The Korea National Health and Nutrition Examination Survey 2007–2010
Published online September 30, 2018
© 2018 Journal of Obesity & Metabolic Syndrome

Wankyo Chung1, Jung Hwan Park2, Ohk-Hyun Ryu3, Jae Myung Yu3, Hyung Joon Yoo3,4, Shinje Moon3,*

1Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Korea; 2Department of Internal Medicine, Hanyang University College of Medicine, Seoul, Korea; 3Department of Internal Medicine, Kangnam Sacred Heart Hospital, Hallym University Medical Center, Hallym University College of Medicine, Seoul, Korea; 4Department of Internal Medicine, CM Hospital, Seoul, Korea
*Shinje Moon, https://orcid.org/0000-0003-3298-3630, Department of Internal Medicine, Kangnam Sacred Heart Hospital, Hallym University Medical Center, Hallym University College of Medicine, 1 Singil-ro, Yeongdeungpo-gu, Seoul 07441, Korea, Tel: +82-2-846-5326, Fax: +82-2-846-4669, E-mail: sinjei82@hanmail.net
Received February 12, 2018; Revised April 22, 2018; Accepted July 18, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Background

We aimed at evaluating the effect of the z-score of the log-transformed A Body Shape Index (LBSIZ) on cardiovascular disease (CVD) outcomes according to obesity phenotype.

Methods

Data were collected from the Korea National Health and Nutrition Examination Survey conducted from 2007 to 2010. Obesity was defined as a body mass index above 25 kg/m2 and metabolic abnormality was defined as the presence of two or more metabolic risk factors of the Adult Treatment Panel III definition. The participants were classified by obesity and metabolic healthy status: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO). Each group was further classified into three groups based on the tertile of LBSIZ. A multivariate logistic regression analysis with adjustment for age, sex, smoking status, income, education level, physical activities, alcohol, and energy intake was conducted to evaluate the odds ratio (OR) for CVD events.

Results

In the multivariate logistic regression model, MHO participants who are within the third tertile of LBSIZ had a significantly higher OR for CVD events, whereas those who are within the first and second tertile of LBSIZ were not at high risk of developing CVDs compared to MHNO participants who are within the first tertile of LBSIZ. In addition, a similar increase in the OR was observed in MUNO or MUO participants.

Conclusion

LBSIZ had the lowest risk for CVDs in the first tertile of LBSIZ and a linear relationship with all its tertiles in MHO, MUNO, and MUO participants.

Keywords : Obesity, Metabolically benign, Body mass index, Body constitution, Cardiovascular disease
INTRODUCTION

The World Health Organization (WHO) reported that 13% of the world’s adult population were obese (body mass index [BMI] ≥30 kg/m2) in 2016 and the worldwide prevalence of obesity was nearly tripled between 1975 and 2016.1 According to the Korea National Health and Nutrition Examination Survey (KNHANES), the prevalence of obesity with a BMI ≥25 kg/m2 in Korean adults increased from 25.8% in 1998 to 31.5% in 2014.2 This increase is particularly significant when the association between obesity and poor clinical outcomes, such as type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVDs), stroke, and mortality, is considered.1,36 However, obese individuals do not always have metabolic dysfunction. This type of obesity is referred to as metabolically healthy obesity phenotype. Although, by definition, individuals with metabolically healthy obesity are metabolically healthy, the clinical outcomes of this condition remain controversial. Hamer and Stamatakis7 have conducted a 7-year observational study involving 22,203 participants without CVD, and failed to show the association between metabolically healthy obesity and the risk for CVD or all-cause mortality. In addition, several observational studies have shown that metabolically healthy obesity did not increase the risk for coronary atherosclerosis8 and heart failure.9 On the other hand, several large-scale population-based studies have reported that metabolically healthy obesity is considered as a significant risk factor for CVDs.10,11 The differences in these results suggest that metabolically healthy obesity phenotype represents a heterogeneous group. Therefore, the key factors that determine poor clinical outcomes in individuals with metabolically healthy obesity must be investigated.

In the present study, we focused on the drawback of BMI as a key factor for the heterogeneous prognosis. BMI cannot distinguish between muscle and fat, and it cannot accurately predict the percentage of body fat.12 In addition, several epidemiological studies have reported the limitations of BMI in predicting the risk of heart attack, stroke, and death.1315 To overcome this, Krakauer and Krakauer16 developed A Body Shape Index (ABSI), a new equations for estimating body shape using waist circumference (WC), weight, and height in the U.S. population. However, there have been few studies to examine the usefulness of ABSI in the other ethnic group. Therefore, we used the z-score of the log-transformed A Body Shape Index (LBSIZ) to control for age, sex, and ethnic differences.17 This study aimed at assessing the effect of body shape index on CVD outcomes according to obesity phenotype in a representative sample of the Korean population.

METHODS

Study population

Data were collected from the KNHANES conducted from 2007 to 2010. The surveys were cross-sectional and nationally representative with a multistage and stratified sample design. In total, 33,829 individuals were included in the study. Participants with missing data or those under 20 years of age were excluded. The total number of eligible participants was 21,948 (Fig. 1).

Clinical and laboratory measurements

WC was measured at the midpoint between the lowest border of the rib cage and the upper lateral border of the iliac crest at the end of normal expiration. Blood pressure (BP) was measured three times while the patient is in a sitting position after at least 5 minutes of rest. An average of three recorded BP measurements was used. After an 8-hour overnight fasting, triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and glucose levels were measured using ADVIA 1650 (Siemens, Washington, DC, USA) in 2007 and Hitachi Automatic Analyzer 7600 (Hitachi, Tokyo, Japan) from 2008 to 2010. We used the revised HDL-C values between 2007 and 2010 according to the Korea Centers for Disease Control and Prevention (KCDC) guidelines for consistency in each survey.18,19

Measurement of obesity

In the present study, obesity was defined as a BMI above 25 kg/m2 based on the Asia-Pacific BMI criteria by the WHO Western Pacific Region.20 Because of the lack of a standard definition for metabolic health and obesity21, we used the National Cholesterol Education Program–Adult Treatment Panel III (NCEP–ATP III) criteria for metabolic syndrome, which is the most frequently used definition in recent studies.2127 Briefly, metabolic health was defined as ≤1 of the following NCEP–ATP III criteria, including impaired fasting glucose level (at least 100 mg/dL), hypertension (a systolic BP above 130 mmHg and/or a diastolic BP above 85 mmHg or the use of antihypertensive drugs), TG level of at least 150 mg/dL, and a low level of HDL-C (40 mg/dL in men and 50 mg/dL in women). Due to the collinearity between BMI and WC, the central obesity criterion was not used. The participants were classified based on these criteria: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO).

ABSI is calculated using the following equation16:

ABSI=WC×weight-2/3×height5/6=WC/(BMI2/3×height1/2)

However, because of its statistical limitations in terms of skewness and symmetry, the z-score of the log-transformed ABSI was used. The detailed equation used for the calculation of LBSIZ was described in our previous study.17

Statistical analysis

Summary statistics are presented as mean and standard deviation or prevalence (%). One-way analysis of variance and the Pearson chi-square test were used to compare each variable according to obesity phenotypes. A multivariate logistic regression analysis with adjustment for age, sex, smoking status, income, education level physical activities, alcohol, and energy intake was carried out to evaluate the odds ratio (OR) and 95% confidence interval (CI) for CVD events according to obesity phenotype and LBSIZ. The graphical relationships were also evaluated with restricted cubic spline plots with three knots according to obesity phenotypes. Analyses were performed using IBM SPSS version 24.0 (IBM Corp., Armonk, NY, USA) and the statistical program R (R version 3.1.0, 2014; https://www.r-project.org). A P-value of 0.05 was considered statistically significant.

Ethical considerations

The study protocol was approved by the Institutional Review Board of the KCDC (2007-02CON-04-P, 2008-04EXP-01-C, 2009-01CON-03-2C, 2010-02CON21-C). Written informed consent was obtained from each study participant prior to the survey.

RESULTS

Baseline characteristics of the participants

The data of 21,948 participants were assessed. The general characteristics of the participants according to obesity phenotypes are summarized in Table 1.

Association between obesity phenotypes and risk of CVD events

A total of 883 CVD events were reported. In the multivariate logistic regression model, participants with MHO were at moderate risk of developing CVD compared with participants with MHNO (OR, 1.485; 95% CI, 1.031–2.139), whereas participants with metabolic abnormalities were at high risk regardless of obesity (MUNO: OR, 1.768; 95% CI, 1.373–2.278; MUO: OR, 2.179; 95% CI, 1.664–2.855) (Table 2). Although the CVD events were most frequently reported in MUNO group, MUO showed the highest OR for CVD event after adjustment for the confounding factors.

Risk of developing CVDs according to obesity phenotypes and LBSIZ

We divided the LBSIZ into tertiles and examined the relative risk of developing CVDs across the tertiles based on obesity phenotype. The distribution of LBSIZ was described in Table 3 according to obesity phenotype. In the multivariate logistic regression analysis, MHO participants who are within the third tertiles of LBSIZ had a significantly higher OR for CVDs, whereas those who are within the first and second tertile of LBSIZ were not at risk for CVD compared to MHNO participants who are within the first tertile. In addition, the ORs of the MUNO and MUO participants increased similarly (Fig. 2). Because age and sex ratio were significantly different across the subgroups by obesity phenotypes, further analysis with propensity score matching methods was performed and showed similar results (Table 4). In the restricted cubic spline regression, LBSIZ showed a linear relationship with CVD events according to each obesity phenotype (Fig. 3).

DISCUSSION

In this study, we investigated the risk for CVD events based on MHO phenotype. MHO participants are at moderate risk for CVD than MHNO participants. Notably, we found that LBSIZ had a positive linear relationship with CVD events in MHO participants. Although, the linear relationship disappeared in MUO participants after weighting, similar pattern was shown in MHNO and MUO participants in the unweighted logistic regression analysis and the restricted cubic spline regression.

These results show that patients with MHO phenotype are not homogenous. This heterogeneity might be due to a limitation in the definition of obesity based on BMI, which is used to define the severity of obesity in the general population based on the guidelines of the WHO.20 However, BMI could not accurately obtain body fat mass because body weight cannot be used to differentiate excess fat from bone or muscle mass.28 In addition, BMI does not consider age-related changes in muscle mass.29 Although body fat increases and muscle mass decreases with age, BMI may not reflect the proportional changes in body fat or muscle mass.29,30 Other disadvantages of using BMI are the following: BMI cannot not measure the regional body fat composition, and it does not consider the distribution of fats in terms of sex. In addition, the metabolic effects of fat differ according to its location. For instance, visceral fat is associated with T2DM and CVDs31, whereas subcutaneous fat has beneficial effects on metabolism.32 Therefore, BMI has limitations in terms of classifying obesity. A person with excessive visceral fat and insufficient muscle mass can have a normal BMI but have a high mortality risk.33

A number of studies attempted to investigate obesity using other body index parameters, such as WC, because of the limitations of BMI.34 WC is strongly associated with visceral adiposity and metabolic risk and increased morbidity and mortality.35,36 However, WC could not distinguish subcutaneous fat from visceral fat deposition.37 In addition, there are insufficient age- and sex-specific data that can define obesity.29

ABSI, which is another obesity index, was recently introduced using WC, weight, and height. This index was closely associated with mortality in the United States.16 However, it has limitations in terms of its applicability to different genders and countries.17,38 Moreover, it may have statistical limitations with regard to skewness and symmetry. LBSIZ has recently been proposed to complement and improve ABSI.17 Our previous study has demonstrated that LBSIZ is a standard normalized obesity measurement that is independent of weight, height, and BMI, and it can be age and sex independent. Importantly, while LBSIZ was not correlated with BMI in Korean population17, it had a linear relationship with CVD risks across different obesity phenotype, grouped by BMI and criteria for metabolic syndrome in the present study. This result evidently showed the usefulness of LBSIZ as a complementary measure to overcome the drawbacks of BMI.

Our findings can provide a convincing explanation for the heterogeneous results of the previous studies with regard to the risk of developing CVDs in MHO individuals.8,37,39 Taking these into consideration, the findings show that the limitation of BMI should be considered along with other determining factor, such as total body fat mass, fat distribution or body shape, when predicting cardiovascular risk.

The main strength of the present study is that it was a large-scale observational study with a representative sample. However, there were several limitations in this study as well. First, this is a cross-sectional study. To clarify the relationship between each obesity phenotype and CVD events, further prospective studies must be conducted. Second, because the present study involved a population of Korean adults, the results are applicable only in Koreans. Third, we could not assess data on mortality, and fatal CVD events may have been missed.

In conclusion, we analyzed the association between the CVD outcomes and obesity phenotype by body shape. LBSIZ had a linear relationship in individuals with MHO phenotype. These findings may have implications in terms of CVD risk assessment in MHO group. In addition, the applications of these findings are important to supplement the drawback of BMI in both clinical practice and epidemiologic studies. However, further prospective studies must be conducted to redefine MHO phenotype in view of body shape to validate MHO.

CONFLICTS OF INTEREST

The authors declare no conflict of interest.

Figures
Figure 1. Flowchart for final selection. KNHANES, Korea National Health and Nutrition Examination Survey; CVD, cardiovascular disease.
Figure 2. Odds ratio (OR; 95% confidence interval [CI]) for cardiovascular events in terms of obesity phenotype and tertile of LBSIZ. (A) Unweighted model. (B) Weighted model. Adjusted for age, sex, family income level, education level, moderate intensity physical activities (>120 minute per week), alcohol consumption, smoking status, and energy intake. LBSIZ, z-score of the log-transformed A Body Shape Index; MHNO, metabolically healthy non-obese; MHO, metabolically healthy obese; MUNO, metabolically unhealthy non-obese; MUO, metabolically unhealthy obese.
Figure 3. Relationship between continuous LBSIZ and the odds ratio for cardiovascular events according to obesity phenotypes. (A) Total. (B) Subgroup by obesity phenotypes. Adjusted for age, sex, family income level, education level, moderate intensity physical activities (>120 minute per week), alcohol consumption, smoking status, and energy intake. LBSIZ, z-score of the log-transformed A Body Shape Index; MUO, metabolically unhealthy obese; MUNO, metabolically unhealthy non-obese; MHO, metabolically healthy obese; MHNO, metabolically healthy non-obese.
Tables

Characteristics of the participants according to obesity phenotypes

Variable MHNO (n=10,134) MHO (n=2,665) MUNO (n=4,822) MUO (n=4,327) P
Male sex 3,766 (37.2) 1,235 (46.3) 2,301 (47.7) 2,093 (48.4) <0.001

Age (yr) 43.7±15.4 45.7±14.6 57.8±14.7 54.4±14.2 <0.001

CVD events* 193 (1.9) 78 (2.9) 334 (6.9) 278 (6.4) <0.001

Smoking 3,801 (37.5) 1,144 (42.9) 2,235 (46.4) 1,967 (45.5) <0.001

BMI (kg/m2) 21.6±2.1 27.1±2.0 22.6±1.8 27.6±2.4 <0.001

Waist circumference (cm) 75.2±7.2 89.0±7.1 80.8±6.9 92.0±7.0 <0.001

LBSIZ −0.2±1.0 −0.2±1.0 0.4±1.0 0.1±0.9 <0.001

Systolic BP (mmHg) 109.6±13.9 115.3±14.1 126.1±18.2 127.1±16.6 <0.001

Diastolic BP (mmHg) 71.1±9.2 75.2±9.2 78.1±10.8 80.8±10.4 <0.001

FBG level (mg/dL) 89.8±11.9 92.7±13.5 106.6±30.8 109.0±29.2 <0.001

HbA1c (%) 7.1±1.8 7.0±1.3 7.4±1.6 7.3±1.4 0.120

Total cholesterol (mg/dL) 180.0±32.8 193.7±35.7 192.7±38.1 198.0±37.6 <0.001

HDL-C (mg/dL) 52.7±10.4 49.6±9.4 42.9±9.2 41.7±8.4 <0.001

TG (mg/dL) 87.8±45.2 107.3±57.8 184.8±131.1 201.8±135.5 <0.001

Metabolic state
 Central obesity 1,252 (12.4) 1,894 (71.1) 1,403 (29.1) 3,544 (81.9) <0.001
 High BP 1,210 (11.9) 529 (19.8) 3,110 (64.5) 3,037 (70.2) <0.001
 Hyperglycemia 710 (7.0) 268 (10.1) 2,520 (52.3) 2,482 (57.4) <0.001
 Low HDL-C level 2,607 (25.7) 755 (28.3) 3,484 (72.3) 3,170 (73.3) <0.001
 High TG level 521 (5.1) 247 (9.3) 2,783 (57.7) 2,800 (64.7) <0.001

Values are presented as number (%) or mean±standard deviation.

Participants who had either myocardial infarction, coronary heart disease, congestive heart failure, cerebrovascular disease, or peripheral arterial disease.

MHNO, metabolically healthy non-obese; MHO, metabolically healthy obese; MUNO, metabolically unhealthy non-obese; MUO, metabolically unhealthy obese; CVD, cardiovascular disease; BMI, body mass index; LBSIZ, z-score of the log-transformed A Body Shape Index; BP, blood pressure; FBG, fasting blood glucose; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride.


Odds ratios for CVD events according to obesity phenotypes

Variable Unweighted model Weighted model


Odds ratio (95% CI) P Odds ratio (95% CI) P
MHNO 1 (Reference) 1 (Reference)

MHO 1.568 (1.194–2.060) 0.001 1.485 (1.031–2.139) 0.034

MUNO 1.758 (1.456–2.122) <0.001 1.768 (1.373–2.278) <0.001

MUO 2.147 (1.770–2.603) <0.001 2.179 (1.664–2.855) <0.001

Adjusted for age, sex, family income level, education level, moderate intensity physical activities (>120 minute per week), alcohol consumption, smoking status, and energy intake.

CVD, cardiovascular disease; CI, confidence interval; MHNO, metabolically healthy non-obese; MHO, metabolically healthy obese; MUNO, metabolically unhealthy non-obese; MUO, metabolically unhealthy obese.


The distribution of LBSIZ by obesity phenotype

Percentile 10th 20th 30th 40th 50th 60th 70th 80th 90th
Total −1.28 −0.84 −0.53 −0.25 0 0.25 0.50 0.81 1.24
MHNO −1.43 −1.02 −0.70 −0.44 −0.19 0.06 0.32 0.60 1.01
MHO −1.47 −0.99 −0.71 −0.46 −0.21 0.04 0.28 0.57 1.00
MUNO −0.90 −0.46 −0.15 0.13 0.37 0.62 0.88 1.19 1.59
MUO −1.08 −0.64 −0.32 −0.08 0.14 0.37 0.59 0.88 1.30

LBSIZ, z-score of the log-transformed A Body Shape Index; MHNO, metabolically healthy non-obese; MHO, metabolically healthy obese; MUNO, metabolically unhealthy non-obese; MUO, metabolically unhealthy obese.


Odds ratio (95% CI) for cardiovascular events in terms of obesity phenotype and tertile of LBSIZ after propensity score matching with age and sex

Variable Odds ratio (95% CI) P
MHNO tertile 1st vs. MHO
 Tertile 1st 1.551 (0.794–3.030) 0.198
 Tertile 2nd 1.462 (0.766–2.791) 0.250
 Tertile 3rd 1.970 (1.103–3.518) 0.022

MHNO tertile 1st vs. MUNO
 Tertile 1st 1.228 (0.733–2.058) 0.435
 Tertile 2nd 1.564 (1.009–2.423) 0.045
 Tertile 3rd 2.157 (1.456–3.194) <0.001

MHNO tertile 1st vs. MUO
 Tertile 1st 1.805 (1.069–3.047) 0.027
 Tertile 2nd 2.079 (1.317–3.281) 0.002
 Tertile 3rd 2.173 (1.395–3.382) 0.001

Adjusted for age, sex, family income level, education level, moderate intensity physical activities (>120 minute per week), alcohol consumption, smoking status, and energy intake.

CI, confidence interval; LBSIZ, z-score of the log-transformed A Body Shape Index; MHNO, metabolically healthy non-obese; MHO, metabolically healthy obese; MUNO, metabolically unhealthy non-obese; MUO, metabolically unhealthy obese.


References
  1. World Health Organization. Fact sheet on obesity and overweight [Internet]. Geneva: World Health Organization; 2017. [cited 2018 May 13].
    Available from: http://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight
  2. Korean Statistical Information Service. Prevalence of obesity in Korea [Internet]. Daejeon: Statistics Korea; 2018. [cited 2018 Feb 5].
    Available from: http://kosis.kr/statHtml/statHtml.do?orgId=117&tblId=DT_11702_N101#
  3. Solomon CG, and Manson JE. Obesity and mortality: a review of the epidemiologic data. Am J Clin Nutr 1997;66:1044S-1050S.
    Pubmed CrossRef
  4. Krauss RM, Winston M, Fletcher BJ, and Grundy SM. Obesity: impact on cardiovascular disease. Circulation 1998;98:1472-6.
    Pubmed CrossRef
  5. Moon S, Oh CM, Choi MK, Park YK, Chun S, and Choi M et al. The influence of physical activity on risk of cardiovascular disease in people who are obese but metabolically healthy. PLoS One 2017;12:e0185127.
    Pubmed KoreaMed CrossRef
  6. Wallström P, Bjartell A, Gullberg B, Olsson H, and Wirfält E. A prospective Swedish study on body size, body composition, diabetes, and prostate cancer risk. Br J Cancer 2009;100:1799-805.
    Pubmed KoreaMed CrossRef
  7. Hamer M, and Stamatakis E. Metabolically healthy obesity and risk of all-cause and cardiovascular disease mortality. J Clin Endocrinol Metab 2012;97:2482-8.
    Pubmed KoreaMed CrossRef
  8. Mørkedal B, Vatten LJ, Romundstad PR, Laugsand LE, and Janszky I. Risk of myocardial infarction and heart failure among metabolically healthy but obese individuals: HUNT (Nord-Trøndelag Health Study), Norway. J Am Coll Cardiol 2014;63:1071-8.
    Pubmed CrossRef
  9. Voulgari C, Tentolouris N, Dilaveris P, Tousoulis D, Katsilambros N, and Stefanadis C. Increased heart failure risk in normal-weight people with metabolic syndrome compared with metabolically healthy obese individuals. J Am Coll Cardiol 2011;58:1343-50.
    Pubmed CrossRef
  10. Ogorodnikova AD, Kim M, McGinn AP, Muntner P, Khan U, and Wildman RP. Incident cardiovascular disease events in metabolically benign obese individuals. Obesity (Silver Spring) 2012;20:651-9.
    Pubmed KoreaMed CrossRef
  11. Kramer CK, Zinman B, and Retnakaran R. Are metabolically healthy overweight and obesity benign conditions? A systematic review and meta-analysis. Ann Intern Med 2013;159:758-69.
    Pubmed CrossRef
  12. Rey-López JP, de Rezende LF, Pastor-Valero M, and Tess BH. The prevalence of metabolically healthy obesity: a systematic review and critical evaluation of the definitions used. Obes Rev 2014;15:781-90.
    Pubmed CrossRef
  13. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, and Korinek J et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond) 2008;32:959-66.
    Pubmed KoreaMed CrossRef
  14. Schneider HJ, Friedrich N, Klotsche J, Pieper L, Nauck M, and John U et al. The predictive value of different measures of obesity for incident cardiovascular events and mortality. J Clin Endocrinol Metab 2010;95:1777-85.
    Pubmed CrossRef
  15. Flegal KM, Graubard BI, Williamson DF, and Gail MH. Excess deaths associated with underweight, overweight, and obesity. JAMA 2005;293:1861-7.
    Pubmed CrossRef
  16. Krakauer NY, and Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One 2012;7:e39504.
    Pubmed KoreaMed CrossRef
  17. Chung W, Park CG, and Ryu OH. Association of a new measure of obesity with hypertension and health-related quality of life. PLoS One 2016;11:e0155399.
    Pubmed KoreaMed CrossRef
  18. Korea Centers for Disease Control and Prevention. The guideline of the forth Korea National Health and Nutrition Examination Survey (KNHANES-IV). Cheongju: Korea Centers for Disease Control and Prevention; 2009.
  19. Korea Centers for Disease Control and Prevention. The guideline of the fifth Korea National Health and Nutrition Examination Survey (KNHANES-V-1). Cheongju: Korea Centers for Disease Control and Prevention; 2010.
  20. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157-63.
    Pubmed CrossRef
  21. Jung CH, Lee WJ, and Song KH. Metabolically healthy obesity: a friend or foe?. Korean J Intern Med 2017;32:611-21.
    Pubmed KoreaMed CrossRef
  22. Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation 2002;106:3143-421.
    Pubmed CrossRef
  23. Yoon JW, Jung CH, Kim MK, Park HE, Park KS, and Jang HC et al. Influence of the definition of “metabolically healthy obesity” on the progression of coronary artery calcification. PLoS One 2017;12:e0178741.
    Pubmed KoreaMed CrossRef
  24. Jung CH, Lee MJ, Kang YM, Jang JE, Leem J, and Hwang JY et al. The risk of incident type 2 diabetes in a Korean metabolically healthy obese population: the role of systemic inflammation. J Clin Endocrinol Metab 2015;100:934-41.
    Pubmed CrossRef
  25. Heianza Y, Arase Y, Tsuji H, Fujihara K, Saito K, and Hsieh SD et al. Metabolically healthy obesity, presence or absence of fatty liver, and risk of type 2 diabetes in Japanese individuals: Toranomon Hospital Health Management Center Study 20 (TOPICS 20). J Clin Endocrinol Metab 2014;99:2952-60.
    Pubmed CrossRef
  26. Hinnouho GM, Czernichow S, Dugravot A, Nabi H, Brunner EJ, and Kivimaki M et al. Metabolically healthy obesity and the risk of cardiovascular disease and type 2 diabetes: the Whitehall II cohort study. Eur Heart J 2015;36:551-9.
    Pubmed KoreaMed CrossRef
  27. Eshtiaghi R, Keihani S, Hosseinpanah F, Barzin M, and Azizi F. Natural course of metabolically healthy abdominal obese adults after 10 years of follow-up: the Tehran Lipid and Glucose Study. Int J Obes (Lond) 2015;39:514-9.
    Pubmed CrossRef
  28. Rahman M, and Berenson AB. Accuracy of current body mass index obesity classification for white, black, and Hispanic reproductive-age women. Obstet Gynecol 2010;115:982-8.
    Pubmed KoreaMed CrossRef
  29. Gurunathan U, and Myles PS. Limitations of body mass index as an obesity measure of perioperative risk. Br J Anaesth 2016;116:319-21.
    Pubmed CrossRef
  30. Rothman KJ. BMI-related errors in the measurement of obesity. Int J Obes (Lond) 2008;32:S56-9.
    Pubmed CrossRef
  31. Gastaldelli A, Miyazaki Y, Pettiti M, Matsuda M, Mahankali S, and Santini E et al. Metabolic effects of visceral fat accumulation in type 2 diabetes. J Clin Endocrinol Metab 2002;87:5098-103.
    Pubmed CrossRef
  32. Tran TT, Yamamoto Y, Gesta S, and Kahn CR. Beneficial effects of subcutaneous fat transplantation on metabolism. Cell Metab 2008;7:410-20.
    Pubmed KoreaMed CrossRef
  33. Coutinho T, Goel K, Corrêa de Sá D, Carter RE, Hodge DO, and Kragelund C et al. Combining body mass index with measures of central obesity in the assessment of mortality in subjects with coronary disease: role of “normal weight central obesity”. J Am Coll Cardiol 2013;61:553-60.
    Pubmed CrossRef
  34. Bener A, Yousafzai MT, Darwish S, Al-Hamaq AO, Nasralla EA, and Abdul-Ghani M. Obesity index that better predict metabolic syndrome: body mass index, waist circumference, waist hip ratio, or waist height ratio. J Obes 2013;2013. 269038
    Pubmed KoreaMed CrossRef
  35. Bays H. Central obesity as a clinical marker of adiposopathy; increased visceral adiposity as a surrogate marker for global fat dysfunction. Curr Opin Endocrinol Diabetes Obes 2014;21:345-51.
    Pubmed KoreaMed CrossRef
  36. Chan JM, Rimm EB, Colditz GA, Stampfer MJ, and Willett WC. Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care 1994;17:961-9.
    Pubmed CrossRef
  37. Arnlöv J, Ingelsson E, Sundström J, and Lind L. Impact of body mass index and the metabolic syndrome on the risk of cardiovascular disease and death in middle-aged men. Circulation 2010;121:230-6.
    Pubmed CrossRef
  38. Duncan MJ, Mota J, Vale S, Santos MP, and Ribeiro JC. Associations between body mass index, waist circumference and body shape index with resting blood pressure in Portuguese adolescents. Ann Hum Biol 2013;40:163-7.
    Pubmed CrossRef
  39. Hinnouho GM, Czernichow S, Dugravot A, Batty GD, Kivimaki M, and Singh-Manoux A. Metabolically healthy obesity and risk of mortality: does the definition of metabolic health matter?. Diabetes Care 2013;36:2294-300.
    Pubmed KoreaMed CrossRef


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