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Original Article
Metabolic Risk/Epidemiology Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis
Ji Yoon Kim1orcid, Jimi Choi1, Chantal A. Vella2, Michael H. Criqui3, Matthew A. Allison3, Nam Hoon Kim1orcid
Diabetes & Metabolism Journal 2022;46(5):747-755.
Published online: March 30, 2022
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1Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea

2Department of Movement Sciences, College of Education, Health and Human Sciences, University of Idaho, Moscow, ID, USA

3Division of Preventive Medicine, Department of Family and Preventive Medicine, University of California San Diego, La Jolla, CA, USA

Corresponding author: Nam Hoon Kim Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, 73 Inchon-ro, Seongbuk-gu, Seoul 02841, Korea E-mail:
• Received: October 22, 2021   • Accepted: January 14, 2022

Copyright © 2022 Korean Diabetes Association

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

  • Background
    The weight-adjusted waist index (WWI) reflected body compositional changes with aging. This study was to investigate the association of WWI with abdominal fat and muscle mass in a diverse race/ethnic population.
  • Methods
    Computed tomography (CT) data from 1,946 participants for abdominal fat and muscle areas from the Multi-Ethnic Study of Atherosclerosis (785 Whites, 252 Asians, 406 African American, and 503 Hispanics) were used. Among them, 595 participants underwent repeated CT. The WWI was calculated as waist circumference (cm) divided by the square root of body weight (kg). The associations of WWI with abdominal fat and muscle measures were examined, and longitudinal changes in abdominal composition measures were compared.
  • Results
    In all race/ethnic groups, WWI was positively correlated with total abdominal fat area (TFA), subcutaneous fat area, and visceral fat area, but negatively correlated with total abdominal muscle area (TMA) and abdominal muscle radiodensity (P<0.001 for all). WWI showed a linear increase with aging regardless of race and there were no significant differences in the WWI distribution between Whites, Asians, and African Americans. In longitudinal analyses, over 38.6 months of follow-up, all abdominal fat measures increased but muscle measures decreased, along with increase in WWI. The more the WWI increased, the more the TFA increased and the more the TMA decreased.
  • Conclusion
    WWI showed positive associations with abdominal fat mass and negative associations with abdominal muscle mass, which likely reflects the abdominal compositional changes with aging in a multi-ethnic population.
Excess body fat, assessed regionally or generally, is recognized as a cause and predictor of various metabolic and cardiovascular diseases [1,2]. Therefore, measurement or assessment of body fat has been a critical issue in medical science [3,4]. On the other hand, cumulative evidence has emphasized the role of muscle mass in the development of several diseases [5]. Specifically, low muscle mass and/or strength has been shown to be an independent indicator of fragility, age-related disorders, and even mortality [6]. However, most anthropometric indices do not distinguish between muscle mass and fat mass and thus tend to be proportional to both [7,8]. This is one of the reasons for the seemingly paradoxical relationship between obesity and some health outcomes [9,10].
A newly proposed anthropometric index, weight-adjusted waist index (WWI), was notable for its ability to assess fat and muscle mass reciprocally. Specifically, in elderly individuals over 65 years, WWI was positively associated with total and abdominal fat measures, but negatively associated with appendicular skeletal muscle mass [11]. Furthermore, a linear positive association of WWI with mortality, as well as cardiometabolic morbidity was observed [12], which was not seen with body mass index (BMI) or waist circumference (WC). However, the associations were only validated in Korean elderly, an East Asian race. Given that there exists substantial difference of body composition by races or ethnic groups, we do not have answers whether the association of WWI with fat and muscle mass is also applicable to the other ethnic groups than East Asian, yet.
Given the aforementioned, the current analysis evaluated the associations between WWI and both abdominal fat and muscle mass measures in different racial and ethnic groups, as well as with the longitudinal changes in abdominal fat and muscle mass in the multi-ethnic population cohort.
Study population
The Multi-Ethnic Study of Atherosclerosis (MESA) is a community-based longitudinal cohort study with 6,814 men and women aged 45 to 84 years at baseline, recruited from six United States communities. At this visit, it was comprised of 38% Caucasians, 28% African Americans, 22% Hispanics, and 12% Asians (primarily Chinese). The first examination began in July 2000, and the subsequent examinations were conducted regularly. The latest (6th) examination was from September 2016 to June 2018. Details of the study protocol and design have been published previously [13].
The components of the first examination included personal history, demographic data, socioeconomic status, medications and medical history, occupational and leisure-time physical activity, anthropometry, blood pressure measurement, phlebotomy, spot urine collection for microalbuminuria, and cardiac computed tomography (CT) for coronary calcification and magnetic resonance imaging scans for cardiac structure and function. Informed consent was obtained from all participants at each study clinic.
The abdominal body composition ancillary study consisted of a random subset of 1,975 MESA participants who underwent abdominal CT for measurement of aortic calcium during visit 2 and visit 3 examinations (2002 to 2005). These scans were then interrogated for different body composition measures to include visceral, subcutaneous, and intermuscular fat, as well as lean muscle mass in the abdomen (as described below). This data was used for cross-sectional analyses in the current study. Among the participants who underwent CT during second visit (visit 2), 595 underwent a follow-up CT scan during visit 4 (2005 to 2007), which was used for the longitudinal analyses in this study.
The MESA studies were approved by the Institutional Review Board of each study site and all participants provided written informed consent. The Institutional Review Board of Korea University Anam Hospital also approved this study (IRB number 2021AN0501).
Anthropometric index and variables from abdominal CT scans
Anthropometric measurements included height, body weight, WC, and hip circumference. BMI was calculated as weight (kg) divided by the square of the height (m2). WWI was calculated as WC (cm) divided by the square root of weight in kilograms (√kg).
In the MESA body composition ancillary study, abdominal slices at vertebral level L4/L5 from the abdominal CT were processed using Medical Image Processing, Analysis, and Visualization version 4.1.2 software (MIPAV, National Institutes of Health, Bethesda, MD, USA) that measured the areas of fat, muscle mass, and total tissue by a semiautomated method. The densities of all anatomical structures were measured and expressed in Hounsfield units (HU) for each tissue type. Fat tissue and skeletal muscle tissue were identified as being between −190 and −30 HU and between 0 and 100 HU, respectively [14]. Any densities other than these two HU ranges were labeled as undefined tissue type. Total abdominal fat area (TFA) was subdivided into subcutaneous fat area (SFA) and visceral fat area (VFA) according to the corresponding anatomical location. Muscle area was defined within their unique fascial planes as psoas, rectus abdominis, paraspinal muscle group, and oblique muscle group. Total abdominal muscle area (TMA) was subdivided into muscles of locomotion (psoas muscle) and muscles of stability (rectus abdominis, paraspinal muscle group, and oblique muscle group). Muscle density was the average HU within each muscle’s distinct fascial plane. Inter-rater and intra-rater reliability for total abdominal, subcutaneous, and visceral cavity areas were 0.99 for all measurements. Inter- and intra-rater reliability for all muscle groups ranged from 0.93 to 0.98.
Other variables
Sociodemographic and lifestyle information including age, sex, race, current occupation, family income, insurance status, marital status, detailed history of cigarette smoking and alcohol consumption was obtained from standardized questionnaires. Current and past medical history including medications, treatments, and medical conditions was also obtained. Diabetes mellitus was defined as fasting glucose ≥126 mg/dL or use of glucose-lowering agents. Hypertension was defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg or use of anti-hypertensive medications. Using the Typical Week Physical Activity Survey, participants self-reported their time spent in sedentary behavior and in physical activities.
Statistical analysis
Demographics, laboratory measures, and body composition measures using abdominal CT scans at baseline were presented as mean (standard deviation) or number (percentage). A Pearson correlation analysis was performed to investigate the linear relationship between WWI and both abdominal fat and muscle mass measures. The results are presented as correlation coefficients and corresponding 95% confidence intervals based on Fisher’s r-to-z transformation. To compare the distribution of WWI and BMI according to race, age and sex adjusted z-score for WWI and BMI were calculated by regressing WWI or BMI on age and sex, respectively and standardized residuals of the regression models. Density plot of age-sex or sex adjusted WWI z-score drawn by smooth curves that goes through the tops of the histogram bars were displayed according to race (Caucasian, Asian, African American, and Hispanic) and age (45–54, 55–64, 65–74, and 75–84 years). The difference in mean WWI by race and age was evaluated after adjusting for age and sex (separately) using analysis of covariance. Means of fat and muscle measures according to quartiles of WWI were compared by one-way analysis of variance and used Tukey’s method for post hoc analysis.
Five hundred and ninety-five participants underwent repeated abdominal CT during the 4th MESA examination. The mean interval between the 2nd and 4th examinations was 38.6±3.3 months. The longitudinal changes in the anthropometric index and abdominal fat and muscle area measures were assessed using the paired t-test. To evaluate the association between the change in anthropometric index and change in body composition measures, means of percentage changes in TFA or TMA were compared by quartiles of change in WWI or BMI using analysis of variance with adjustment for age.
Statistical significance was set at P<0.05. All statistical analyses were performed using the SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA) and R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria).
The baseline characteristics of all participants are shown in Table 1. The study participants comprised 785 Caucasians (40.3%), 252 Asians (12.9%), 406 African Americans (20.9%), and 503 Hispanics (25.8%), with mean age, BMI, and WC of 64.7 years, 28.1 kg/m2, and 98.3 cm, respectively. WWI values were approximately normally distributed with a mean of 11.17, standard deviation of 0.91 and range of 8.34 to 14.44 cm/√kg (Supplementary Fig. 1). WWI increased with age in both men and women, and this trend was consistent in all race/ethnic groups (Supplementary Table 1).
WWI is positively associated with abdominal fat area measures but negatively associated with abdominal muscle area measures in all race/ethnic groups
Correlation analyses showed that abdominal fat area measures, including TFA, SFA, and VFA, positively correlated with WWI, whereas all abdominal muscle area measures negatively correlated with WWI, regardless of race/ethnicity (P<0.001 for all analyses) (Supplementary Table 2). Total abdominal muscle radiodensity negatively correlated with WWI, which indicated that the fat component within abdominal muscles was positively associated with WWI.
The association of WWI with abdominal fat and muscle area measures was examined by comparing the mean values and z-scores according to WWI quartiles (Table 2, Supplementary Table 3). The mean values of abdominal fat area z-scores showed increasing trends from the lowest quartile to the highest quartile of WWI, whereas those of muscle area z-scores showed decreasing trends with increasing quartiles of WWI. These results were consistent regardless of ethnicity (P for trends <0.001 for all ethnic groups), and age (Fig. 1). Fig. 2 is a representative figure showing the contrasting relationship between abdominal fat and muscle mass measures with WWI. In contrast, BMI or WC did not reflect the fat and muscle area measures differently. Not only fat area but also muscle area showed positive correlation with WC and BMI (Table 2).
Age, not race/ethnicity, determines WWI
Given that the trend for increasing WWI with increasing age, we examined the distribution of WWI according to age in all participants in the MESA cohort. Fig. 3A shows the distribution curves by age groups and reveals that WWI is positively associated with age. Compared to the 45 to 54 years age group, mean WWI was significantly higher with increasing age groups (P=0.001 for 55–64 years group and P<0.001 for 65–74 and 75–84 years groups). This relationship was consistent in each ethnic group (Supplementary Fig. 2); however, there were no significant associations between BMI and age.
On the other hand, distribution of WWI was similar between ethnic groups. For example, WWI of Asians did not significantly deviate from that of Caucasians and African Americans (P=0.512, and P=0.720, respectively) (Fig. 3B). However, BMI of Asians was significantly lower than that of other ethnic groups (P<0.001 for all).
WWI reflects body compositional changes with aging
We examined the longitudinal changes in CT-derived fat and muscle area measures and their associations with anthropometric index. Over 38.6 months, the mean TFA and VFA increased, but the TMA decreased in all participants (Supplementary Table 4). Mean SFA increased significantly only in men, not in women. During the same period, BMI did not change, but the WC and WWI significantly increased. Increase in WWI was commonly observed in all age groups, but the direction of changes in BMI and WC differed with age (Supplementary Table 5).
To examine whether longitudinal changes in WWI were associated with increasing fat mass and decreasing muscle area, we examined the percentage changes of TFA and TMA according to quartiles of changes in WWI (delta WWI) (Fig. 4). We found that as WWI increased, TFA increased (P<0.001) and TMA decreased, although changes in TMA were not statistically significant (P=0.963). In contrast, as BMI increased, both TFA and TMA increased (P<0.001 and P=0.056, respectively).
In this study, we found that WWI was positively associated with abdominal fat measures, but negatively with abdominal muscle mass measures, regardless of race/ethnicity. Also, we showed that WWI increased with age in all races, but there was a small difference in the mean values and distribution between races. Finally, we determined that WWI also reflects abdominal composition changes with aging, i.e., increase in abdominal fat mass and decrease in muscle mass. Collectively, WWI could be an indicator of fat and muscle composition changes related to aging commonly applicable to multiethnic groups.
As the role of muscle or lean mass in the development of age-related disorders has been elucidated, measurement of muscle mass has become increasingly important as a measure of body composition. However, estimation of muscle mass without a special device is difficult, and no anthropometric index so far has been sufficiently verified as an indicator of muscle mass [15,16]. Similar to a previous paper on WWI in Korean elderly people, the results presented above indicate that WWI was differentially associated with fat and muscle mass in a multi-ethnic population. However, this study is limited because the analyses were based solely on abdominal CT scan-derived measures, and not on the whole-body composition data. Although we found weaker associations of WWI with muscle mass measures compared to that of a previous Korean study, we showed the similar patterns with the previous study. These findings suggest that the WWI could serve as a body composition index, which is not limited to East Asians, and can be commonly applied in diverse populations.
Furthermore, we found that WWI was negatively associated with abdominal muscle radiodensity (represented by HU), indicating a positive association between WWI and fat infiltration in the muscles. Interestingly, the correlation of abdominal muscle radiodensity with WWI (r=−0.516) was higher than that with BMI (r=−0.242) or WC (r=−0.300) (data not shown in the results). Because abdominal muscle radiodensity has been found to be an indicator of myosteatosis, metabolic derangement such as insulin resistance and alterations in adipokines, as well as mortality in cancer patients [17-20], the high correlation of WWI with abdominal muscle mass radiodensity suggests WWI could be an indicator of diverse health outcomes. Although we were unable to assess the associations of WWI with other organ radiodensities, such as liver radiodensity, these associations should be tested in future studies for the broader applications of WWI as a health indicator.
Another significant finding of this study was that the racial difference of WWI distribution was small. BMI, a widely-used anthropometric index, revealed fundamental differences between races with different body shape or different fat distribution; hence, different standards are applied according to race [21,22]. For example, there are different BMI and WC cut-points for defining obesity in Asians compared to Caucasians [23,24]. In this multiethnic cohort, we also found certain differences by races (e.g., mean BMI was significantly lower in Asians than in Whites). However, in the case of WWI, there was no significant difference in the mean and distribution between Caucasians and Asians. This suggests that WWI could be a universal health index applicable to various races/ethnic groups. This finding can be explained partially by the fact that WWI reflects the ratio of fat and muscle mass, but not the absolute fat amount, and shows a direct proportional relationship with aging.
Regardless of race, WWI was observed to increase with age. In addition, the longitudinal increase in WWI reflected the age-related changes in abdominal fat and muscle composition. Since this is a short-term change spanning over an average of 38 months, a long-term study will be needed to establish an association that is more reliable.
The previous study on WWI only included elderly individuals over 65 years of age, and it was unclear whether the bidirectional association of WWI with fat and muscle mass measures would be reproducible in younger people. This study included 977 participants younger than 65 years (50.2% of total participants) and found that the associations of WWI with fat and muscle measures did not differ with age. Therefore, our data indicated that WWI could be generally applicable to adults of various races and ethnicities, although we still need more evidence with respect to younger adults and adolescents.
This study has several limitations. First, as mentioned earlier, we did not have relevant data regarding the overall body fat and appendicular muscle mass of participants. However, we assumed that the relationship between WWI and various muscle measures, including appendicular muscle mass, would be similar based on previous study results. Second, the number of Asians was relatively small in the current cohort data. Third, the association of WWI with health outcomes remains to be determined. Further studies will be needed to provide the relevant information regarding compatible cut-offs for WWI.
In conclusion, this study showed that WWI had a differential association with fat and muscle mass measures in a multi-ethnic population. Moreover, WWI was closely associated with age and age-related body compositional changes. Further investigation is needed for the validation of this index as a predictor of various health outcomes.
Supplementary materials related to this article can be found online at
Supplementary Table 1.
Mean±standard deviation of weight-adjusted waist index according to age and race
Supplementary Table 2.
Correlation coefficient (95% CI) between weight-adjusted waist index and body composition measures according to ethnicity
Supplementary Table 3.
Mean values of muscle and fat measures by weight-adjusted waist index quartiles
Supplementary Table 4.
A longitudinal analysis of body composition cohort (from 2nd exam to 4th exam)
Supplementary Table 5.
Longitudinal changes of anthropometric index by age groups
Supplementary Fig. 1.
(A) Distribution of weight-adjusted waist index (WWI) (mean±standard deviation, 11.17±0.91; median, 11.05 [interquartile range, 10.53 to 11.68]; range, 8.34 to 14.44). (B) quantile-quantile (Q-Q) plot.
Supplementary Fig. 2.
Distribution of weight-adjusted waist index (WWI) by age in each ethnic group.


No potential conflict of interest relevant to this article was reported.


Conception or design: N.H.K.

Acquisition, analysis, or interpretation of data: J.C., C.A.V., M.H.C., M.A.A., N.H.K.

Drafting the work or revising: J.Y.K., N.H.K.

Final approval of the manuscript: N.H.K.



The authors thank the all investigators, staffs, and participants of the MESA study.
Fig. 1.
(A) Total abdominal fat area (TFA) and (B) total abdominal muscle area (TMA) by quartiles of weight-adjusted waist index (WWI) in adults older and younger than 65 years. aStatistically different from the 1st quartile group, bStatistically different from the 1st and 2nd quartile groups, cStatistically different from the 1st, 2nd, and 3rd quartile groups.
Fig. 2.
Association between weight-adjusted waist index (WWI) and (A) total abdominal fat area (TFA) and (B) total abdominal muscle area (TMA).
Fig. 3.
Distribution of body mass index (BMI) and weight-adjusted waist index (WWI) by (A) age and (B) race/ethnicity.
Fig. 4.
The association between change of (A) weight-adjusted waist index (WWI), (B) body mass index (BMI) and age-adjusted change of body composition measures. TFA, total abdominal fat area; TMA, total abdominal muscle area.
Table 1.
Baseline characteristics of participants (n=1,946)
Variable Value
Age, yr 64.7±9.7
 Female 962 (49.4)
 Male 984 (50.6)
 White 785 (40.3)
 Asian 252 (12.9)
 African American 406 (20.9)
 Hispanic 503 (25.8)
Body mass index, kg/m2 28.1±5.2
Waist circumference, cm 98.3±14.1
Fasting glucose, mg/dL 98.3±27.7
Cholesterol, mg/dL
 Total 189.7±35.5
 High-density lipoprotein 51.5±15.2
 Low-density lipoprotein 112.0±31.4
Triglyceride, mg/dL 133.6±95.0
Creatinine, mg/dL 0.98±0.22
Systolic blood pressure, mm Hg 124.1±20.8
Hypertension 914 (47.4)
Diabetes mellitus 277 (14.2)
Current smoker 225 (11.6)
Current alcohol consumption 1,004 (51.7)
MVPA (MET), min/wk 4,939.9±4,778.8
Weight-adjusted waist index, cm/√kg 11.17±0.91
Abdominal computed tomography measures, cm2
 Total abdominal fat area 419.5±160.2
 Subcutaneous fat area 253.7±117.7
 Visceral fat area 147.9±69.1
 Total abdominal muscle area 98.3±27.6
 Locomotor abdominal muscle area 23.7±7.4
 Stability abdominal muscle area 74.6±21.8

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

MVPA, moderate and vigorous physical activity; MET, metabolic equivalent.

Table 2.
Mean values of muscle and fat measure z-scores by quartiles of WWI, BMI, and WC
All (n=1,946) z-TFA z-SFA z-VFA z-TMA z-TMR
Quartiles of WWI
 1st –0.64±0.79a –0.55±0.83a –0.65±0.74a 0.04±0.94a 0.39±0.91a
 2nd –0.17±0.69b –0.16±0.70b –0.16±0.85b 0.13±1.03a 0.12±0.93b
 3rd 0.20±0.82c 0.17±0.84c 0.17±0.92c –0.04±0.95b –0.09±0.96c
 4th 0.84±1.09d 0.74±1.17d 0.66±0.99d –0.14±1.06b –0.44±1.03d
P valuee <0.001 <0.001 <0.001 <0.001 <0.001
P valuef <0.001 <0.001 <0.001 <0.001 <0.001
Quartiles of BMI
 1st –0.93±0.54a –0.87±0.55a –0.83±0.63a –0.32±0.88a 0.35±0.90a
 2nd –0.23±0.46b –0.20±0.47b –0.28±0.69b –0.05±0.92b 0.15±0.93b
 3rd 0.31±0.49c 0.25±0.6c 0.28±0.78c 0.22±1.01c –0.01±0.97c
 4th 1.40±0.88d 1.33±1.01d 0.86±0.98d 0.17±1.10c –0.52±0.99d
P valuee <0.001 <0.001 <0.001 <0.001 <0.001
P valuef <0.001 <0.001 <0.001 <0.001 <0.001
Quartiles of WC
 1st –0.94±0.54a –0.84±0.58a –0.88±0.59a –0.22±0.92a 0.46±0.85a
 2nd –0.24±0.44b –0.22±0.49b –0.27±0.66b –0.01±0.92b 0.17±0.94b
 3rd 0.34±0.50c 0.28±0.62c 0.27±0.79c 0.12±1.02b –0.12±0.95c
 4th 1.41±0.85d 1.31±1.00d 0.92±0.94d 0.11±1.10b –0.54±0.98d
P valuee <0.001 <0.001 <0.001 <0.001 <0.001
P valuef <0.001 <0.001 <0.001 <0.001 <0.001

Values are presented as mean±standard deviation.

WWI, weight-adjusted waist index; BMI, body mass index; WC, waist circumference; TFA, total abdominal fat area; SFA, subcutaneous fat area; VFA, visceral fat area; TMA, total abdominal muscle area; TMR, total abdominal muscle radiodensity.

a,b,c,d Different letters indicate significant differences between groups (P<0.05) by Tukey’s method,

e P value by ANOVA,

f P value for linear trend test.

  • 1. Amato MC, Guarnotta V, Giordano C. Body composition assessment for the definition of cardiometabolic risk. J Endocrinol Invest 2013;36:537-43.PubMed
  • 2. Britton KA, Massaro JM, Murabito JM, Kreger BE, Hoffmann U, Fox CS. Body fat distribution, incident cardiovascular disease, cancer, and all-cause mortality. J Am Coll Cardiol 2013;62:921-5.ArticlePubMedPMC
  • 3. Snijder MB, van Dam RM, Visser M, Seidell JC. What aspects of body fat are particularly hazardous and how do we measure them? Int J Epidemiol 2006;35:83-92.ArticlePubMed
  • 4. Romero-Corral A, Lopez-Jimenez F, Sierra-Johnson J, Somers VK. Differentiating between body fat and lean mass-how should we measure obesity? Nat Clin Pract Endocrinol Metab 2008;4:322-3.ArticlePubMedPDF
  • 5. Choi KM. Sarcopenia and sarcopenic obesity. Endocrinol Metab (Seoul) 2013;28:86-9.ArticlePubMedPMC
  • 6. Fielding RA, Vellas B, Evans WJ, Bhasin S, Morley JE, Newman AB, et al. Sarcopenia: an undiagnosed condition in older adults: current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc 2011;12:249-56.ArticlePubMedPMC
  • 7. Romero-Corral A, Somers VK, Sierra-Johnson J, Jensen MD, Thomas RJ, Squires RW, et al. Diagnostic performance of body mass index to detect obesity in patients with coronary artery disease. Eur Heart J 2007;28:2087-93.ArticlePubMed
  • 8. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, 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.ArticlePubMedPMCPDF
  • 9. Kim NH, Lee J, Kim TJ, Kim NH, Choi KM, Baik SH, et al. Body mass index and mortality in the general population and in subjects with chronic disease in korea: a nationwide cohort study (2002-2010). PLoS One 2015;10:e0139924.ArticlePubMedPMC
  • 10. Antonopoulos AS, Oikonomou EK, Antoniades C, Tousoulis D. From the BMI paradox to the obesity paradox: the obesitymortality association in coronary heart disease. Obes Rev 2016;17:989-1000.ArticlePubMed
  • 11. Kim NH, Park Y, Kim NH, Kim SG. Weight-adjusted waist index reflects fat and muscle mass in the opposite direction in older adults. Age Ageing 2021;50:780-6.ArticlePubMedPDF
  • 12. Park Y, Kim NH, Kwon TY, Kim SG. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep 2018;8:16753.ArticlePubMedPMCPDF
  • 13. Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol 2002;156:871-81.ArticlePubMed
  • 14. Goodpaster BH, Thaete FL, Kelley DE. Composition of skeletal muscle evaluated with computed tomography. Ann N Y Acad Sci 2000;904:18-24.ArticlePubMed
  • 15. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412-23.PubMedPMC
  • 16. Rolland Y, Czerwinski S, Abellan Van Kan G, Morley JE, Cesari M, Onder G, et al. Sarcopenia: its assessment, etiology, pathogenesis, consequences and future perspectives. J Nutr Health Aging 2008;12:433-50.ArticlePubMedPMCPDF
  • 17. Amini B, Boyle SP, Boutin RD, Lenchik L. Approaches to assessment of muscle mass and myosteatosis on computed tomography: a systematic review. J Gerontol A Biol Sci Med Sci 2019;74:1671-8.ArticlePubMedPMCPDF
  • 18. Tilves C, Zmuda JM, Kuipers AL, Carr JJ, Terry JG, Wheeler V, et al. Associations of thigh and abdominal adipose tissue radiodensity with glucose and insulin in nondiabetic Africanancestry men. Obesity (Silver Spring) 2020;28:404-11.ArticlePubMedPMCPDF
  • 19. Vella CA, Cushman M, Van Hollebeke RB, Allison MA. Associations of abdominal muscle area and radiodensity with adiponectin and leptin: the multiethnic study of atherosclerosis. Obesity (Silver Spring) 2018;26:1234-41.ArticlePubMedPMCPDF
  • 20. Sjoblom B, Gronberg BH, Wentzel-Larsen T, Baracos VE, Hjermstad MJ, Aass N, et al. Skeletal muscle radiodensity is prognostic for survival in patients with advanced non-small cell lung cancer. Clin Nutr 2016;35:1386-93.ArticlePubMed
  • 21. Rahman M, Temple JR, Breitkopf CR, Berenson AB. Racial differences in body fat distribution among reproductive-aged women. Metabolism 2009;58:1329-37.ArticlePubMedPMC
  • 22. Zhu S, Heymsfield SB, Toyoshima H, Wang Z, Pietrobelli A, Heshka S. Race-ethnicity-specific waist circumference cutoffs for identifying cardiovascular disease risk factors. Am J Clin Nutr 2005;81:409-15.PubMed
  • 23. Obesity: preventing and managing the global epidemic: report of a WHO consultation. World Health Organ Tech Rep Ser 2000;894:i-xii. 1-253.PubMed
  • 24. Seo MH, Lee WY, Kim SS, Kang JH, Kang JH, Kim KK, et al. 2018 Korean Society for the Study of Obesity guideline for the management of obesity in Korea. J Obes Metab Syndr 2019;28:40-5.ArticlePubMedPMC

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      Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis
      Diabetes Metab J. 2022;46(5):747-755.   Published online March 30, 2022
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    Diabetes Metab J : Diabetes & Metabolism Journal