Journal of Obesity & Metabolic Syndrome



September, 2023 | Vol.32 No.3

J Obes Metab Syndr 2023; 32(2): 130-140

Published online June 30, 2023

Copyright © Korean Society for the Study of Obesity.

A Novel Anthropometric Parameter, Weight-Adjusted Waist Index Represents Sarcopenic Obesity in Newly Diagnosed Type 2 Diabetes Mellitus

Min Jeong Park1, Soon Young Hwang2, Nam Hoon Kim1, Sin Gon Kim1, Kyung Mook Choi1, Sei Hyun Baik1, Hye Jin Yoo1,*

1Division of Endocrinology and Metabolism, Department of Internal Medicine, 2Department of Biostatistics, Korea University College of Medicine, Seoul, Korea

Correspondence to:
Hye Jin Yoo
Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea
Tel: +82-2-2626-3045
Fax: +82-2-2626-1096

Received: January 19, 2023; Reviewed : February 2, 2023; Accepted: March 30, 2023

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: As the metabolic significance of sarcopenic obesity (SO) is revealed, finding an appropriate index to detect SO is important, especially for type 2 diabetes mellitus (T2DM) patients with accompanying metabolic dysfunction.
Methods: Participants (n=515) from the Korea Guro Diabetes Program were included to compare how well waist circumference (WC), waist hip ratio (WHR), waist height ratio (WHtR), and the weight-adjusted waist index (WWI) predict SO in newly diagnosed T2DM patients. Sarcopenia was defined based on guidelines from the 2019 Asian Working Group for Sarcopenia as both low muscle mass (appendicular skeletal muscle [ASM]/height2 <7.0 kg/m2 for men, <5.4 kg/m2 for women) and strength (handgrip strength <28.0 kg for men, <18.0 kg for women) and/or reduced physical performance (gait speed <1.0 m/sec). Obesity was defined as a WC ≥90 cm in men and ≥85 cm in women. The WHR, WHtR, and WWI were calculated by dividing the WC by the hip circumference, height, and √ weight, respectively.
Results: The WC, WHR, and WHtR correlated positively with the fat and muscle mass represented by truncal fat amount (TFA) and ASM, whereas the WWI was proportional to the TFA and inversely related to ASM. Of the four indices, the WWI showed the highest area under the receiver operative characteristic curve for SO. The WWI also exhibited a positive correlation with albuminuria and the mean brachial-ankle pulse wave velocity, especially in patients aged ≥65 years.
Conclusion: The WWI is the preferable anthropometric index for predicting SO in T2DM patients, and it might be a proper index for predicting cardiometabolic risk factors in elderly people.

Keywords: Type2 diabetes mellitus, Sarcopenic obesity, Anthropometric index

Ever since the significant role that a reduction in muscle mass and strength play in metabolic dysfunction was revealed,1-3 sarcopenia has received attention as an important disease to treat. The co-existence of obesity and sarcopenia, two serious pathologic conditions, is called sarcopenic obesity (SO); the prevalence of SO increases with a sedentary lifestyle, a high calorie diet, and increased age.4,5 SO is not only highly related to the prevalence of metabolic syndrome but also synergistically increases the risk of premature mortality compared with obesity or sarcopenia alone.6-9 The presence of SO in patients with type 2 diabetes mellitus (T2DM) further increases the risk of metabolic disturbance. Previous studies have reported that T2DM patients with SO showed higher insulin resistance, a greater incidence of nonalcoholic fatty liver disease, and greater albuminuria than T2DM patients without SO.10-12 This relationship emphasizes the importance of detecting and treating SO as early as possible and calls for the development of an adequate anthropometric index that can distinguish SO from simple obesity or sarcopenia, particularly in patients who already have metabolic risk factors such as T2DM.

Although body mass index (BMI) has been widely used because it is simple to calculate, it cannot distinguish abdominal/visceral fat and fat/muscle mass, which have different metabolic risks. Therefore, there has been a call for a new index to predict metabolic risk and mortality in elderly people, who often suffer from SO.13,14 Waist circumference (WC), which accurately reflects abdominal obesity, has been verified to predict cardiovascular (CV) risk and CV mortality, but it shows limitations in reflecting low muscle mass.13,15 Other indices have been suggested for measuring low muscle mass, including the circumference of the calf or mid upper arm,16-18 but their low sensitivity and difficulty in measurement prevent them from being widely used.19

The weight-adjusted waist index (WWI) is a recently developed anthropometric index that standardizes the WC with the body weight, and it has shown effectiveness in predicting premature all-cause mortality, CV mortality, and T2DM.20 Furthermore, in a previous Korean cohort study, the WWI demonstrated a remarkable association with high fat mass and low muscle mass.21 WC and WC-derived indices, the waist hip ratio (WHR) and waist height ratio (WHtR), have been analyzed for metabolic risk and mortality prediction as alternative body composition indices,15,22 but they have never been evaluated as parameters of SO.

Therefore, we sought an appropriate anthropometric index to discriminate SO and predict SO-related metabolic risks in newly diagnosed T2DM patients by comparing several indices. We also compared the usefulness of each index across different age groups.

Study design and participants

We analyzed the Korea Guro Diabetes Program (KGDP) cohort, which contains data from newly diagnosed T2DM patients. T2DM was defined as a glycosylated hemoglobin (HbA1c) level of more than 6.5%. We recruited 515 participants from September 2014 to July 2021. All patients underwent blood and urine sample collection, anthropometric measurements, whole-body dual-energy X-ray absorptiometry (DEXA), and a diabetes mellitus complication study at their initial visit. Medical histories and lifestyle information were collected using a detailed questionnaire and personal interview. All participants submitted informed consent, and this study was approved by the Korea University Institutional Review Board (2014GR0140).

Anthropometric, laboratory, and body composition measurements

BMI was calculated as weight (kg)/height (m)2, and WC was measured at the midpoint between the lower edge of the rib and the top of the iliac crest. The hip circumference (HC) was obtained at the widest portion of the buttocks when the patient was in the standing position, following World Health Organization guidelines.23 All blood samples were obtained in the morning after a 12-hour overnight fast and were immediately stored at –80 °C for subsequent assays. The detailed method used to assess the laboratory parameters, including high-sensitivity C-reactive protein (hs-CRP), HbA1c, and albuminuria, was previously described in the KGDP cohort study.11 The estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease equation.24 Each patient underwent whole-body DEXA using fan-beam technology (Hologic Discovery A; Hologic) to measure muscle and fat mass. The appendicular skeletal muscle (ASM, kg) mass is the sum of the lean muscle mass in a patient’s arms and legs, and ASM/height (m)2 was calculated by dividing ASM with square of height to diagnose SO. The truncal fat amount (TFA, g) was measured by DEXA to quantify the fat mass. The WHR was defined as the WC (cm)/HC (cm), and the WHtR was calculated as the WC (cm) divided by the height (cm). The WWI was defined as the WC (cm)/√ weight (√kg).20 As complication studies of T2DM, the carotid intima-media thickness (CIMT) was measured using high-resolution B-mode ultrasonography (EnVisor; Philips Healthcare) with a 5 to 12 MHz transducer 1 to 3 cm proximal to the carotid bifurcation.25 The brachial-ankle pulse wave velocity (baPWV) was measured after a 5-minute rest using a BP-203RPE II volume plethysmographic apparatus (Colin) with the patient in a supine position. The means of the left and right baPWV and CIMT values were defined as the mean baPWV and CIMT, respectively.

Definition of sarcopenic obesity

In this study, the components of sarcopenia were measured and defined according to the 2019 consensus update of the Asian Working Group for Sarcopenia (AWGS)26: low muscle mass plus low muscle strength and/or reduced physical performance. The cutoff value for low muscle mass was a DEXA-measured ASM/height2 <7.0 kg/m2 for men and <5.4 kg/m2 for women.26 Muscle strength was measured by using a dynamometer (Takei) to assess the handgrip strength of both hands with full elbow extension; the maximum value was obtained during two or three trials. Low muscle strength was defined as a handgrip <28.0 kg for men and <18.0 kg for women.26 Physical performance was determined by the usual gait speed and regarded as low when the speed was <1.0 m/sec.26 Obesity was defined as a WC ≥90 cm in men and ≥85 cm in women.27 SO was diagnosed when both sarcopenia and obesity were present.

Statistical analysis

Each variable was assessed for a normal distribution. The data are expressed as the mean±standard deviation. Comparisons of subjects with and without SO were performed using Student t-tests and Mann-Whitney U-tests. Associations between the baseline characteristics of each participant and the body composition indices (WC, WHR, WHtR, and WWI) were estimated using a Spearman correlation analysis. Those correlations were subsequently analyzed by age group (age ≥65 years, age <65 years). We use a scatterplot to show the relationships between variables. Multivariate logistic regression analyses were performed to calculate the odds ratios (ORs) of SO based on tertiles of WC, WHR, WHtR, and WWI. Model 1 was adjusted for age and sex, and model 2 was further adjusted for smoking, alcohol use, and physical activity. Model 3 was further adjusted for the systolic blood pressure, HbA1c, and eGFR. Receiver operating characteristic (ROC) curve analyses were done to assess how well each index predicted SO, and pairwise comparisons of the area under the curve (AUC) for each index were performed using the DeLong test method. We used the Youden J-index to determine optimal cutoff values that maximize sensitivity and specificity. All statistical analyses were performed using SPSS software version 20.0 (SPSS Inc.), and statistical significance was regarded as a P<0.05.

The baseline characteristics of newly diagnosed T2DM patients with or without SO are shown in Table 1. Among the participants, 46 subjects were diagnosed with SO. The population with SO was older than that without SO (59.70 years vs. 53.63 years) and had higher WHR and WWI values than those without SO (WHR: 0.95 vs. 0.92; WWI: 11.01 vs. 10.73). In addition, SO patients had higher baPWV than subjects without SO (1,625.68 vs. 1,515.27).

Comparison of the correlations between WC, WHR, WHtR, and WWI and the body composition measurements

Correlation analyses between WC, WHR, WHtR, and WWI and the baseline characteristics of the study population are shown in Table 2. The WWI correlated positively with age (r=0.150, P=0.001), whereas WC, WHR, and WHtR were negatively associated with age. An increase in the WWI, WC, WHR, and WHtR correlated with increases in aspartate aminotransferase, hs-CRP, and homeostatic model assessment for insulin resistance levels. The relationships between the anthropometric parameters and the mass of fat and muscle are presented in Fig. 1. WC, WHR and WHtR showed significant positive correlations with both the TFA (WC: r=0.756, P<0.001; WHR: r=0.584, P<0.001; WHtR: r=0.797, P<0.001) and the ASM (WC: r=0.473, P<0.001; WHR: r=0.307, P<0.001; WHtR: r=0.094, P=0.034), suggesting that those indicators cannot appropriately reflect reduced muscle mass in obese patients. However, the WWI showed a remarkable positive correlation with the TFA (r=0.501, P<0.001) and a negative correlation with the ASM (r=–0.272, P<0.001). Therefore, this novel anthropometric index might be useful for detecting SO because it correlates with both a reduction of muscle mass and a growth of fat mass.

ORs of SO according to WC, WHR, WHtR, and WWI tertiles, the ROC curve analysis, and comparison of the AUC of each index for SO

Supplementary Table 1 shows the ORs of SO based on the increment of tertiles of WC, WHR, WHtR, and WWI. In the fully adjusted model, WWI showed the highest OR of SO among four indices (WWI [OR, 5.70; 95% confidence interval (CI), 2.04 to 15.94], WHR [OR, 4.40; 95% CI, 1.64 to 11.79], and WHtR [OR, 3.46; 95% CI, 1.20 to 9.98]). WC did not show statistical significance (OR, 1.46; 95% CI, 0.61 to 3.50; P=0.40). The ROC curves for WC, WHR, WHtR, and WWI in predicting SO are presented in Fig. 2. The AUC of the WWI was 0.684, which was the highest value among the indices; the AUC values of WC, WHR, and WHtR were 0.563, 0.656, and 0.584, respectively. The optimal cutoff value of the WWI for SO was 10.87 (sensitivity: 63%, specificity: 63%), whereas those of WC, WHtR, and WHR were 90.15, 0.55, and 0.93, respectively (sensitivity [WC, 61%; WHtR, 57%; WHR, 61%] and specificity [WC, 57%; WHtR, 58%; WHR, 62%]). The differences in the AUC values for each index in predicting SO are presented in Table 3. The WWI and WC showed a noteworthy difference of area (0.121; 95% CI, 0.037 to 0.204), as did the WWI and WHtR (0.100; 95% CI, 0.040 to 0.160), and WC and WHR (0.093; 95% CI, 0.044 to 0.143).

Comparison of WC, WHR, WHtR, and WWI in their correlation with metabolic parameters stratified by age group (<65 and ≥65 years)

Body composition changes were stratified according to age, so we performed a correlation analysis stratified by age in patients <65 and ≥65 years; the results are presented in Supplementary Table 2. Among them, the association between WC, WHR, WHtR, and WWI and albuminuria and mean baPWV are illustrated in Fig. 3. The WWI correlated significantly with albuminuria in patients 65 years and older (r=0.215, P=0.031), but no association was observed in the group aged <65 years (r=0.031, P=0.543). Meanwhile, WC showed no relationship with albuminuria in either age group, and the WHR showed a positive relationship with albuminuria in patients younger than 65 years (r=0.106, P=0.037) but no association in patients aged ≥65 years (r=0.142, P=0.158). Furthermore, although the WHR and WHtR had no significant correlation with the mean baPWV in either age group and WC was associated with the mean baPWV only in the younger group, the WWI showed a significant association with mean baPWV in the elderly group (r=0.260, P=0.009) but not in the younger group (r=0.042, P=0.409). The WWI also showed an age-dependent differential correlation with low density lipoprotein cholesterol (age ≥65 years: r=0.203, P=0.042; age <65 years: r=0.026, P=0.600) and triglycerides (age ≥65 years: r=0.281, P=0.004; age <65 years: r=–0.077, P=0.126), whereas WC, WHR, and WHtR revealed no differential relationship with those factors according to the age groups . Furthermore, the WWI showed an inverse relationship with the mean and max handgrip strength in the group aged ≥65 years. These results indicate the potential of the WWI as a proper index to reflect cardiometabolic risk and loss of muscle function, especially in elderly people.

In this study, we have examined the suitability of the WWI as an index to discriminate SO and other accompanying metabolic disturbances in newly diagnosed T2DM patients. Elderly patients who are undergoing age-related body composition changes (including high visceral fat and low muscle mass) are more desirable group than younger patients for applying the WWI to evaluate body composition and metabolic risks. The WWI showed a negative correlation with muscle mass, in contrast to WC, WHR, and WHtR, which exhibited a positive relationship with both fat mass and muscle mass. In addition, the WWI predicts SO better than the other three indicators, with an approximately five times higher OR in the highest tertile of the WWI compared with the lowest. The highest AUC in the ROC analysis for SO was shown in the WWI, which shows its statistical superiority to the AUC of the WHtR and WC. Furthermore, the WWI exhibited an obvious relationship with various cardiometabolic risk factors, such as arterial stiffness and albuminuria, especially in elderly people, a vulnerable group at higher risk than younger people for the progression of SO.

Previous studies have sought an index to reflect a metabolically hazardous fat distribution, which includes mostly visceral fat, and overcome the limitations of the BMI. WC and various WC-derived measures (such as the WHR and WHtR) were previously inspected for their ability to predict metabolic disease and mortality.15,28-30 As the role of muscle composition has been increasingly emphasized, demand has also risen for an index that can distinguish between muscle and fat mass. Although computed tomography (CT), magnetic resonance imaging, and DEXA are the most accurate methods for measuring muscle mass, their high cost and radiation exposure limit their usage, which leads to an underdiagnosis of sarcopenia. A simple, cost-effective, and reliable anthropometric index is needed to screen for and diagnose sarcopenia. In 2019, the AWGS suggested calf circumference (CC) or the Simple Five-Item (SARC-F) questionnaire as screening tools for sarcopenia in community health care settings.26,31,32 Despite the decent correlation between CC and low muscle mass, that index cannot reflect the increments in visceral fat percentage that are usually present in elderly patients with sarcopenia. In addition, the SARC-F questionnaire depends on self-reporting, which reduces its objectivity and introduces potential bias.

The WWI was developed by Park et al.20 and is distinguished by its association with both low muscle mass and high fat, in contrast to other indices, which reflect only one or the other. In a previous study based on 602 subjects from the Ansan Geriatric cohort, the WWI also correlated positively with the total and visceral fat areas measured by CT, and it had a negative relationship with the ASM and ASM/height2.21 However, prior studies did not test the power of the WWI to discriminate low muscle function (muscle strength and physical performance) due to limited data. Therefore, the ability of the WWI to identify sarcopenia, which involves the loss of both muscle mass and function, had not been verified. As Stenholm et al.4 underlined, a decline in muscle strength and function is more important to the pathologic outcomes of sarcopenia than the simple reduction in muscle mass. In this research, we evaluated the strength of various indices to identify SO, which defined using muscle mass, strength, and physical performance data according to the 2019 AWGS definition. We defined obesity by WC instead of BMI because the predictability of BMI for body composition and obesity-related mortality is reduced in elderly participants, who comprise most SO patients.6,33 In addition, WC is closely related to metabolic risk in Asian people who accumulate intra-abdominal fat predominantly but have a relatively low BMI.27,34 In this study, we also verified the relationship between the WWI and metabolic disturbances, represented by albuminuria and the mean baPWV, in addition to finding that the WWI correlates with T2DM, CV-related risk, and mortality. Albuminuria is an indicator of systemic inflammation and insulin resistance and a risk factor for chronic kidney disease (CKD), hypertension (HTN), and CV disease.35,36 Arterial stiffness, as measured by the pulse wave velocity, reflects an increased risk of coronary heart disease, CKD, and HTN.37,38 The WWI revealed a noticeably better relationship with albuminuria and mean baPWV than WC, the WHR, and the WHtR, especially in people ≥65 years of age, which suggests that the WWI is suitable for predicting metabolic risk in elderly people.

Recent studies have reported that T2DM patients, who already have a high metabolic risk, face further metabolic hazard when they are also diagnosed with SO. Kim et al.11 demonstrated that insulin resistance is increased in T2DM patients with SO compared to those with simple obesity or low muscle mass. In previous studies, high incidences of albuminuria or CV disease were reported in T2DM patients with SO.10,39 Therefore, applying the WWI to screen for SO in T2DM patients is important for preventing severe metabolic dysfunction and diabetic complications.

The predominant mechanism of sarcopenia is understood to be age-related sex hormone decline, and it is also characterized by mitochondrial dysfunction and an increase in protein proteolysis.38 Leptin and proinflammatory cytokines secreted from adipocytes also play important roles that lead to muscle catabolism.6 A reduction in muscle mass and function promotes physical inactivity, and the disuse of muscles sequentially causes motor neuron loss. Therefore, identifying sarcopenia alongside obesity using a simple and practical method and offering early interventions could prevent a vicious cycle, particularly in the elderly population. The WWI is easy to calculate and can predict sarcopenia and obesity together simply by measuring a person’s WC and weight. We expect this marker to encourage clinicians to assess their patients for SO. A possible mechanism for the SO-predicting power of the WWI can be found in the process of formula development. Park et al.20, who developed the WWI formula, removed the effect of weight on WC with a regression method, in the same way that the BMI was developed by removing the effect of height on weight. The BMI estimates that a person with a specific weight and short stature has higher risks than a person with the same weight and tall stature, and the WWI similarly assesses that a person with the certain WC and lower weight has more risks than a person with the same WC and more weight. For this reason, the WWI might well reflect the risk of abdominal obesity with low weight, which represents SO.

This study has several limitations. First, it was a cross-sectional study, so the temporal effects of the body composition measures in each index on metabolic disturbance were difficult to evaluate. Second, it was conducted in a relatively small cohort of Asian people from a single center. Larger longitudinal studies of people from various ethnicities are needed to estimate the causal relationships and effects across age and ethnic groups. Nonetheless, to the best of our knowledge, this is the first study to compare the use of anthropometric measures to identify SO, defined by muscle mass, strength, and physical performance. In addition, we estimated the correlation of each index with metabolic risks and then analyzed those findings by age group. The BMI has limitations, particularly in elderly patients who are diagnosed with both SO and SO-related metabolic disturbances; therefore, the WWI’s close association with the metabolic risk of elderly patients suggests that it can probably serve as a marker of SO and overcome the limitations of the BMI.

In conclusion, we have demonstrated the strength of the WWI as a parameter for SO and SO-related metabolic disturbances in newly diagnosed T2DM patients. The WWI might well predict metabolic dysfunction associated with SO, especially in an elderly population. Large-scale prospective studies are needed to verify the WWI as a discriminator of SO and to determine its clinical implications for fine risk stratification that could be used to predict cardiometabolic diseases in different age groups.

This research was supported by a Korea Medical Device Development Fund grant funded by the Korean government (Ministry of Science and ICT, Ministry of Trade, Industry and Energy, Ministry of Health & Welfare, Ministry of Food and Drug Safety) (Project Number: 9991007469, KMDF_PR_20200901_0233) and by the National Research Foundation of Korea funded by the Ministry of Education of Korea (2021R1A2C2008792).

Study concept and design: MJP, NHK, SGK, and HJY; acquisition of data: KMC and HJY; analysis and interpretation of data: MJP, SYH, and HJY; drafting of the manuscript: MJP, SYH, and HJY; critical revision of the manuscript: KMC and SHB; statistical analysis: MJP and SYH; obtained funding: HJY; administrative, technical, or material support: NHK, SGK, KMC, SHB, and HJY; and study supervision: KMC, SHB, and HJY.

Fig. 1. Correlation analysis between anthropometric indices (A: waist circumference [WC], B: waist hip ratio [WHR], C: waist height ratio [WHtR], D: weight-adjusted waist index [WWI]) and body composition measurements (truncal fat amount [TFA], appendicular skeletal muscle [ASM]).
Fig. 2. Receiver operating characteristic curve analyses for waist circumference (WC), waist hip ratio (WHR), waist height ratio (WHtR), and weight-adjusted waist index (WWI) to predict sarcopenic obesity.
Fig. 3. Correlation analysis between anthropometric indices (A: waist circumference [WC], B: waist hip ratio [WHR], C: waist height ratio [WHtR], D: weight-adjusted waist index [WWI]) and albuminuria, mean brachial-ankle pulse wave velocity (baPWV) by age group (age ≥ 65 years, age < 65 years).

Baseline characteristics of subjects with and without sarcopenic obesity who were newly diagnosed with type 2 diabetes mellitus

Characteristic Subjects without sarcopenic obesity (n=460) Subjects with sarcopenic obesity (n=46) P
Male sex 233 (50.65) 25 (54.30) 0.633
Age (yr) 53.63 ± 11.79 59.70 ± 11.59 < 0.001
BMI (kg/m2) 26.45 ± 4.27 25.73 ± 2.57 0.095
WC (cm) 90.33 ± 11.51 91.25 ± 7.09 0.433
WHR (cm/cm) 0.92 ± 0.06 0.95 ± 0.05 < 0.001
WHtR (cm/cm) 0.55 ± 0.06 0.56 ± 0.00 0.262
WWI (cm/√kg) 10.73 ± 0.62 11.01 ± 0.38 < 0.001
Systolic BP (mmHg) 128.47 ± 14.51 130.89 ± 11.78 0.274
Diastolic BP (mmHg) 83.17 ± 11.49 83.57 ± 10.88 0.825
AST (IU/L) 27 (22–33) 24 (21–33) 0.239
ALT (IU/L) 27 (18–40) 21 (15–45) 0.122
FPG (mg/dL) 135 (119–169) 137 (116–168) 0.815
HbA1c (%) 6.90 (6.50–8.50) 6.80 (6.50–8.20) 0.890
eGFR (mL/min/1.73 m2) 100.34 (87.82–115.34) 101.97 (82.04–115.97) 0.945
Albuminuria (mg/g) 10.90 (7.31–24.47) 10.73 (7.34–18.81) 0.566
hs-CRP (mg/dL) 1.14 (0.57–2.72) 1.12 (0.60–2.46) 0.967
Total cholesterol (mg/dL) 184 (158–215) 179 (157–216) 0.674
Triglyceride (mg/dL) 131 (88–198) 122 (80–173) 0.392
LDL-C (mg/dL) 113 (90–136) 121 (84–137) 0.645
HDL-C (mg/dL) 48 (41–57) 46 (42–60) 0.723
HOMA-IR 1.50 (1.05–2.27) 1.36 (0.94–2.91) 0.854
Mean CIMT (mm) 0.60 (0.52–0.68) 0.60 (0.58–0.68) 0.213
Mean baPWV (cm/sec) 1,468.50 (1,317.30–1,651.50) 1,532.50 (1,412.00–1,770.00) 0.032
Dyslipidemia 206 (44.78) 22 (47.83) 0.692
Hypertension 182 (39.57) 19 (41.30) 0.818
Current smoker 196 (42.61) 21(45.65) 0.692
Current alcohol user 263 (57.17) 24 (52.17) 0.514
Regular exercise 195 (42.39) 27 (58.70) 0.034
CVD history 57 (12.39) 6 (13.04) 0.898

Values are presented as number (%), mean± standard deviation, or median (interquartile range).

BMI, body mass index; WC, waist circumference; WHR, waist hip ratio; WHtR, waist height ratio; WWI, weight-adjusted waist index; BP, blood pressure; AST, aspartate aminotransferase; ALT, alanine aminotransferase; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; eGFR, estimated glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment for insulin resistance; CIMT, carotid intima-media thickness; baPWV, brachial-ankle pulse wave velocity; CVD, cardiovascular disease.

Correlation analysis between body composition indices (WC, WHR, WHtR, WWI) and metabolic- and sarcopenia-related subject characteristics

Variable WC WHR WHtR WWI

r P r P r P r P
Age (yr) –0.194 < 0.001 –0.096 0.030 –0.044 0.323 0.150 0.001
BMI (kg/m2) 0.841 < 0.001 0.590 < 0.001 0.863 < 0.001 0.389 < 0.001
WC (cm) - - 0.840 < 0.001 0.862 < 0.001 0.551 < 0.001
WHR (cm/cm) 0.840 < 0.001 - - 0.751 < 0.001 0.686 < 0.001
WHtR (cm/cm) 0.862 < 0.001 0.737 < 0.001 - - 0.770 < 0.001
WWI (cm/√kg) 0.587 < 0.001 0.686 < 0.001 0.781 < 0.001 - -
Systolic BP (mmHg) 0.271 < 0.001 0.241 < 0.001 0.269 < 0.001 0.183 < 0.001
Diastolic BP (mmHg) 0.212 < 0.001 0.221 < 0.001 0.130 0.003 0.058 0.193
AST (IU/L) 0.264 < 0.001 0.173 < 0.001 0.255 < 0.001 0.133 0.003
ALT (IU/L) 0.309 < 0.001 0.222 < 0.001 0.256 < 0.001 0.083 0.062
FPG (mg/dL) 0.133 0.003 0.097 0.030 0.000 0.994 –0.071 0.111
HbA1c (%) 0.122 0.006 0.097 0.029 0.047 0.288 –0.025 0.574
eGFR (mL/min/1.73 m2) –0.012 0.792 –0.019 0.679 –0.040 0.366 –0.078 0.083
Albuminuria (mg/g) 0.085 0.061 0.109 0.016 0.110 0.015 0.069 0.127
hs-CRP (mg/L) 0.287 < 0.001 0.218 < 0.001 0.297 < 0.001 0.171 < 0.001
Total cholesterol (mg/dL) 0.621 0.502 0.025 0.572 0.003 0.955 –0.004 0.928
Triglyceride (mg/dL) 0.207 < 0.001 0.170 < 0.001 0.134 0.003 –0.017 0.708
LDL-C (mg/dL) 0.037 0.410 0.040 0.369 0.019 0.679 0.038 0.402
HDL-C (mg/dL) –0.270 < 0.001 –0.184 < 0.001 –0.169 < 0.001 0.016 0.717
HOMA-IR 0.463 < 0.001 0.345 < 0.001 0.502 < 0.001 0.244 < 0.001
Mean CIMT (mm) 0.093 0.040 0.109 0.016 0.063 0.164 0.056 0.215
Mean baPWV (cm/sec) –0.111 0.013 –0.032 0.483 –0.016 0.717 0.104 0.021
Mean handgrip strength (kg) –0.007 0.882 0.021 0.638 –0.040 0.381 –0.057 0.209
Max handgrip strength (kg) –0.017 0.709 0.010 0.831 –0.056 0.214 –0.067 0.140
Walking speed (m/sec) –0.112 0.013 –0.117 0.009 –0.008 0.862 –0.017 0.701

P-values were obtained using a Spearman partial correlation coefficient analysis.

WC, waist circumference; WHR, waist hip ratio; WHtR, waist height ratio; WWI, weight-adjusted waist index; BMI, body mass index; BP, blood pressure; AST, aspartate aminotransferase; ALT, alanine aminotransferase; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; eGFR, estimated glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment for insulin resistance; CIMT, carotid intimamedia thickness; baPWV, brachial-ankle pulse wave velocity.

Pairwise comparison of the area under the receiver operating curves for WC, WHR, WHtR, and WWI to predict sarcopenic obesity

Variable Difference between area (95% CI) P
WC-WHR 0.093 (0.044 to 0.143) < 0.001
WC-WHtR 0.021 (–0.030 to 0.072) 0.425
WC-WWI 0.121 (0.037 to 0.204) 0.004
WHR-WHtR 0.072 (0.003 to 0.141) 0.035
WHR-WWI 0.028 (–0.044 to 0.099) 0.453
WHtR-WWI 0.100 (0.040 to 0.160) 0.001

WC, waist circumference; WHR, waist hip ratio; WHtR, waist height ratio; WWI, weightadjusted waist index; CI, confidence interval.

  1. Nishikawa H, Asai A, Fukunishi S, Nishiguchi S, Higuchi K. Metabolic syndrome and sarcopenia. Nutrients 2021;13:3519.
    Pubmed KoreaMed CrossRef
  2. Kim SH, Jeong JB, Kang J, Ahn DW, Kim JW, Kim BG, et al. Association between sarcopenia level and metabolic syndrome. PLoS One 2021;16:e0248856.
    Pubmed KoreaMed CrossRef
  3. Wijarnpreecha K, Panjawatanan P, Thongprayoon C, Jaruvongvanich V, Ungprasert P. Sarcopenia and risk of nonalcoholic fatty liver disease: a meta-analysis. Saudi J Gastroenterol 2018;24:12-7.
    Pubmed KoreaMed CrossRef
  4. Stenholm S, Harris TB, Rantanen T, Visser M, Kritchevsky SB, Ferrucci L. Sarcopenic obesity: definition, cause and consequences. Curr Opin Clin Nutr Metab Care 2008;11:693-700.
    Pubmed KoreaMed CrossRef
  5. Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol 1998;147:755-63.
    Pubmed CrossRef
  6. Lim S, Kim JH, Yoon JW, Kang SM, Choi SH, Park YJ, et al. Sarcopenic obesity: prevalence and association with metabolic syndrome in the Korean Longitudinal Study on Health and Aging (KLoSHA). Diabetes Care 2010;33:1652-4.
    Pubmed KoreaMed CrossRef
  7. Stephen WC, Janssen I. Sarcopenic-obesity and cardiovascular disease risk in the elderly. J Nutr Health Aging 2009;13:460-6.
    Pubmed CrossRef
  8. Han E, Lee YH, Kim G, Kim SR, Lee BW, Kang ES, et al. Sarcopenia is associated with albuminuria independently of hypertension and diabetes: KNHANES 2008-2011. Metabolism 2016;65:1531-40.
    Pubmed CrossRef
  9. Yin T, Zhang JX, Wang FX, Zhao JH, Zhao Y, Liu L, et al. The association between sarcopenic obesity and hypertension, diabetes, and abnormal lipid metabolism in Chinese adults. Diabetes Metab Syndr Obes 2021;14:1963-73.
    Pubmed KoreaMed CrossRef
  10. Takahashi F, Hashimoto Y, Kaji A, Sakai R, Okamura T, Hamaguchi M, et al. Sarcopenic obesity is associated with macroalbuminuria in patients with type 2 diabetes: a cross-sectional study. Endocr J 2021;68:781-9.
    Pubmed CrossRef
  11. Kim JA, Hwang SY, Chung HS, Kim NH, Seo JA, Kim SG, et al. Proportion and characteristics of the subjects with low muscle mass and abdominal obesity among the newly diagnosed and drug-naïve type 2 diabetes mellitus patients. Diabetes Metab J 2019;43:105-13.
    Pubmed KoreaMed CrossRef
  12. Su X, Xu J, Zheng C. The relationship between non-alcoholic fatty liver and skeletal muscle mass to visceral fat area ratio in women with type 2 diabetes. BMC Endocr Disord 2019;19:76.
    Pubmed KoreaMed CrossRef
  13. Sanada K, Chen R, Willcox B, Ohara T, Wen A, Takenaka C, et al. Association of sarcopenic obesity predicted by anthropometric measurements and 24-y all-cause mortality in elderly men: the Kuakini Honolulu Heart Program. Nutrition 2018;46:97-102.
    Pubmed CrossRef
  14. 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.
    Pubmed KoreaMed CrossRef
  15. Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol 2008;61:646-53.
    Pubmed CrossRef
  16. Hwang AC, Liu LK, Lee WJ, Peng LN, Chen LK. Calf circumference as a screening instrument for appendicular muscle mass measurement. J Am Med Dir Assoc 2018;19:182-4.
    Pubmed CrossRef
  17. Wijnhoven HA, van Bokhorst-de van der Schueren MA, Heymans MW, de Vet HC, Kruizenga HM, Twisk JW, et al. Low mid-upper arm circumference, calf circumference, and body mass index and mortality in older persons. J Gerontol A Biol Sci Med Sci 2010;65:1107-14.
    Pubmed KoreaMed CrossRef
  18. Das A, Saimala G, Reddy N, Mishra P, Giri R, Kumar A, et al. Mid-upper arm circumference as a substitute of the body mass index for assessment of nutritional status among adult and adolescent females: learning from an impoverished Indian state. Public Health 2020;179:68-75.
    Pubmed KoreaMed CrossRef
  19. Rolland Y, Lauwers-Cances V, Cournot M, Nourhashémi F, Reynish W, Rivière D, et al. Sarcopenia, calf circumference, and physical function of elderly women: a cross-sectional study. J Am Geriatr Soc 2003;51:1120-4.
    Pubmed CrossRef
  20. 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.
    Pubmed KoreaMed CrossRef
  21. 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.
    Pubmed CrossRef
  22. 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;13:275-86.
    Pubmed CrossRef
  23. Nishida C, Ko GT, Kumanyika S. Body fat distribution and noncommunicable diseases in populations: overview of the 2008 WHO expert consultation on waist circumference and waist-hip ratio. Eur J Clin Nutr 2010;64:2-5.
    Pubmed CrossRef
  24. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 2006;145:247-54.
    Pubmed CrossRef
  25. Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, et al. 2019 ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J 2020;41:255-323.
    Pubmed CrossRef
  26. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc 2020;21:300-7.
    Pubmed CrossRef
  27. Kim BY, Kang SM, Kang JH, Kang SY, Kim KK, Kim KB, et al. 2020 Korean Society for the Study of Obesity guidelines for the management of obesity in Korea. J Obes Metab Syndr 2021;30:81-92.
    Pubmed KoreaMed CrossRef
  28. Choi MK, Han YA, Roh YK. Utility of obesity indicators for metabolically healthy obesity: an observational study using the Korean National Health and Nutrition Examination Survey (2009-2010). BMC Public Health 2014;14:1166.
    Pubmed KoreaMed CrossRef
  29. Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk: a review of the literature. Eur J Clin Nutr 2010;64:16-22.
    Pubmed CrossRef
  30. Vazquez G, Duval S, Jacobs DR Jr, Silventoinen K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis. Epidemiol Rev 2007;29:115-28.
    Pubmed CrossRef
  31. Kawakami R, Murakami H, Sanada K, Tanaka N, Sawada SS, Tabata I, et al. Calf circumference as a surrogate marker of muscle mass for diagnosing sarcopenia in Japanese men and women. Geriatr Gerontol Int 2015;15:969-76.
    Pubmed CrossRef
  32. Lu JL, Ding LY, Xu Q, Zhu SQ, Xu XY, Hua HX, et al. Screening accuracy of SARC-F for sarcopenia in the elderly: a diagnostic meta-analysis. J Nutr Health Aging 2021;25:172-82.
    Pubmed CrossRef
  33. Enzi G, Gasparo M, Biondetti PR, Fiore D, Semisa M, Zurlo F. Subcutaneous and visceral fat distribution according to sex, age, and overweight, evaluated by computed tomography. Am J Clin Nutr 1986;44:739-46.
    Pubmed CrossRef
  34. World Health Organization, International Association for the Study of Obesity, International Obesity Taskforce. The Asia-Pacific perspective: redefining obesity and its treatment. Health Communications; 2000.
  35. Basi S, Fesler P, Mimran A, Lewis JB. Microalbuminuria in type 2 diabetes and hypertension: a marker, treatment target, or innocent bystander? Diabetes Care 2008;31 Suppl 2:S194-201.
    Pubmed CrossRef
  36. Han E, Kim MK, Jang BK, Kim HS. Albuminuria is associated with steatosis burden in patients with type 2 diabetes mellitus and nonalcoholic fatty liver disease. Diabetes Metab J 2021;45:698-707.
    Pubmed KoreaMed CrossRef
  37. Vasan RS, Pan S, Xanthakis V, Beiser A, Larson MG, Seshadri S, et al. Arterial stiffness and long-term risk of health outcomes: the Framingham Heart Study. Hypertension 2022;79:1045-56.
    Pubmed KoreaMed CrossRef
  38. Shah AS, Gidding SS, El Ghormli L, Tryggestad JB, Nadeau KJ, Bacha F, et al. Relationship between arterial stiffness and subsequent cardiac structure and function in young adults with youth-onset type 2 diabetes: results from the TODAY study. J Am Soc Echocardiogr 2022;35:620-8.
    Pubmed KoreaMed CrossRef
  39. Fukuda T, Bouchi R, Takeuchi T, Tsujimoto K, Minami I, Yoshimoto T, et al. Sarcopenic obesity assessed using dual energy X-ray absorptiometry (DXA) can predict cardiovascular disease in patients with type 2 diabetes: a retrospective observational study. Cardiovasc Diabetol 2018;17:55.
    Pubmed KoreaMed CrossRef