INTRODUCTION

The incidence of metabolic syndrome (MS) is increasing worldwide. MS refers to a collection of metabolic abnormalities, including visceral obesity, hyperglycemia, dyslipidemia, and hypertension. It is known as a predisease state and leads to increased risk of cardiovascular disease, type 2 diabetes mellitus, and cancer.

The predominant characteristic of MS is insulin resistance [1] and the most prevalent form of MS is associated with abdominal obesity, especially when accompanied by deposition of visceral adipose tissue [2]. Visceral fat is closely linked to insulin resistance and chronic metabolic disease.

Skeletal muscle comprises a large percentage of body mass and is the most abundant insulin-sensitive tissue [3]. It also plays an important role in maintenance of systemic glucose metabolism [4]. Therefore, loss of skeletal muscle mass (SMM) and skeletal muscle resistance to insulin associated with the aging process or obesity may be fundamental to metabolic dysregulation and may contribute to the development of MS [5]. Potential mechanisms contributing to reduced insulin signaling and action in skeletal muscle include adipose tissue expansion and increased levels of inflammatory adipokines, increased renin-angiotensin-aldosterone system activity, decreased muscle mitochondrial oxidative capacity, increased intramuscular lipid accumulation, and increased levels of reactive oxygen species [5].

Methods for estimating body composition, including SMM and body fat mass (BFM), should be valid and reliable. Bioelectrical analysis (BIA) has recently become a widely accepted method for estimation of body composition and is relatively simple, quick, and noninvasive [6]. Eight-polar BIA offers valid and accurate estimates of total and appendicular body composition when validated against DXA [7].

This study was performed to clarify the clinical role of SMM in development of MS using different anthropometric parameters estimated by multifrequency bioelectrical impedance analysis.

METHODS

Study design

This study is a retrospective cohort study of 1,042 subjects ranging in age from 20 to 75 years old who visited the Yeungnam University Health Promotion Center from June 1, 2008 to June 30, 2010. Clinical data, including demographic factors, past medical history, laboratory findings, and anthropometric parameters were collected at baseline. Twenty-four months after baseline, metabolic parameters and development of MS were assessed. Mean total follow-up period was 28.7±5.4 months. Preexisting MS was excluded. Chronic diseases that can affect SMM were also excluded, including severe anemia (hemoglobin <8 mg/dL), chronic kidney disease (serum creatinine >1.5 mg/dL), abnormal liver function test (total bilirubin, direct bilirubin, aspartate aminotransferase, or alanine aminotransferase >2 folds of upper normal limits), and abnormal tumor markers (α-fetoprotein >15 mg/mL, carbohydrate antigen 19-9 >37 U/mL, carcinoembryonic antigen >10 mg/mL, prostate specific antigen >4 mg/mL in male, or cancer antigen 125 >35 U/mL in female). After excluding 204 subjects, 838 subjects (mean age, 46.9±9.9 years; male:female, 477:361) were included in the study. Study protocol was approved by the Institutional Review Board of Yeungnam University Medical Center. Because this was a retrospective study, the board deemed it exempt from informed consent requirements.

Analytical methods

Height, body weight (BW), and waist circumference (WC) were measured and body mass index (BMI) was calculated by dividing the weight (kg) with height square (m²). WC was measured using a soft tape midway between the lowest rib and the iliac crest while participants were standing. Systolic and diastolic BPs were measured using a standard sphygmomanometer after at least 10 minutes of rest. Blood was drawn for evaluation of metabolic, biochemical, and hematological parameters after overnight fasting for 10 to 12 hours. Fasting plasma glucose (FPG), total cholesterol (TC), triglyceride (TG), and high density lipoprotein cholesterol (HDL-C) were measured using the hexokinase method (AU 5400 Autoanalyser; Olympus, Tokyo, Japan). Low density lipoprotein cholesterol (LDL-C) levels were calculated according to the Friedewald formula. Hemoglobin A1c (HbA1c) was measured using high performance liquid chromatography (HLC-723 G7; Tosoh Corp., Tokyo, Japan) according to the standardized Diabetes Control and Complications Trial assay.

Measurement of anthropometric parameters using body impedance analysis

Bioelectrical impedance was estimated using InBody 720 (Biospace Inc., Seoul, Korea), a multifrequency BIA device, after overnight fasting for at least 8 hours. Study subjects were instructed to stand upright and grasp the handles of the analyzer, thereby putting both palms, thumbs, and anterior and posterior aspects of each sole of the foot in contact an 8-polar tactile-electrode. Impedance values for five segments (arms, trunk, and legs) were measured at frequencies of 1, 5, 20, 50, 500, and 1,000 kHz through the 8-polar tactile-electrode. Based on these impedance values, SMM (kg), total BFM (kg), and visceral fat area (VFA) at the umbilicus level (cm²) were calculated. Skeletal muscle parameters based on these variables were defined as follows, in order to estimate not only of the effect of absolute SMM but also the effect of relative ratio to body composition:

Percent of SMM (SMM%, %): SMM (kg)/weight (kg)

Skeletal muscle index (SMI, kg/m²): SMM (kg)/height (m²)

Skeletal muscle to body fat ratio (MFR): SMM (kg)/BFM (kg)

Skeletal muscle to visceral fat ratio (SVR, kg/cm²): SMM (kg)/VFA (cm²)

Definition of metabolic syndrome

MS was defined based on the modified NCEP-ATP III criteria [8] as any combination of three or more of the following components: 1) abdominal obesity (WC ≥90 cm in males and ≥80 cm in females) based on adjusted Asian-Pacific waist circumference criterion; 2) elevated BP (≥130 mm Hg systolic pressure or ≥85 mm Hg diastolic pressure), or treatment of previously diagnosed hypertension; 3) elevated FPG (≥100 mg/dL) or treatment of diabetes; 4) elevated TG (≥150 mg/dL), or specific treatment for this lipid abnormality; and 5) reduced HDL-C (<40 mg/dL in males and <50 mg/dL in females).

Statistical analysis

Subjects were divided into two groups: those who had developed MS and those who had not developed MS during a 2-year follow-up period. Clinical and anthropometric data are expressed as mean±standard deviation. Comparisons of continuous variables between groups with and without MS were performed using Student t-test. All anthropometric parameters were described in quintiles (data are not shown). Multivariate logistic regression analysis was used to estimate the effects of each anthropometric parameter on development of MS. Odds ratios (ORs) of MS in the fifth quintile were estimated using the first quintile for reference. ORs adjusted for age and gender are presented with 95% confidence intervals (CIs). All statistical analyses were performed using the statistical package for SPSS version 20.0 for Windows (IBM Co., Armonk, NY, USA) with a 5% significance level (P<0.05).

RESULTS

Baseline characteristics

Baseline characteristics are shown in Table 1. Among 838 subjects, 88 subjects (10.5%) developed MS during the 2-year follow-up period. Older males were more prevalent in the MS group compared to the group without MS. Subjects in the MS group had poorer metabolic status (higher BMI, WC, BP, FPG, HbA1c, TC, TG, and LDL-C and lower HDL-C) at baseline than subjects without MS. SMM, BFM, VFA, and SMI were significantly higher in subjects with MS; however, SMM%, MFR, and SVR were significantly higher in subjects without MS.

Proportions of each quintile in subjects with metabolic syndrome

The proportions of each quintile of various anthropometric parameters in subjects with MS are shown in Fig. 1. The fifth quintile of MFR, SMM%, and SVR accounted for the smallest proportion in subjects with MS. In contrast, the fifth quintile of BMI, SMM, VFA, and SMI accounted for the largest proportion in subjects with MS.

Effects of skeletal muscle mass on development of metabolic syndrome

The risk of developing MS increased with increasing quintiles of BMI (OR, 2.24; 95% CI, 1.80 to 2.80), SMM (OR, 1.88; 95% CI, 1.38 to 2.55), VFA (OR, 2.56; 95% CI, 1.98 to 3.32), and SMI (OR, 2.18; 95% CI, 1.63 to 2.92), but was negatively associated with increasing quintiles of SMM% (OR, 0.51; 95% CI, 0.40 to 0.65), MFR (OR, 0.49; 95% CI, 0.39 to 0.62), and SVR (OR, 0.62; 95% CI, 0.49 to 0.77) (Table 2).

ORs for each component of MS according to quintiles of each anthropometric parameter are also shown in Table 2. SMM was positively associated only with high WC (OR, 2.66; 95% CI, 1.73 to 4.10), while SMI was positively associated with high WC (OR, 2.44; 95% CI, 1.65 to 3.62), high BP (OR, 1.31; 95% CI, 1.09 to 1.57), and high TG (OR, 1.40; 95% CI, 1.08 to 1.82). In contrast, SMM% was negatively associated with high WC (OR, 0.7; 95% CI, 0.50 to 0.99), high BP (OR, 0.72; 95% CI, 0.61 to 086), and high TG (OR, 0.77; 95% CI, 0.61 to 0.97). Both SMI and SMM% showed no significant association with dysglycemia or low HDL-C. VFA showed a positive association with components of MS, including high WC (OR, 2.02), high BP (OR, 1.33), dysglycemia (OR, 1.38), and high TG (OR, 1.34). The exception was low HDL-C, which was not significantly associated with VFA. In contrast, MFR showed a significant negative association with high WC (OR, 0.6), high BP (OR, 0.7), dysglycemia (OR, 0.75), and high TG (OR, 0.78), but not low HDL-C. SVR was negatively associated with all components of MS, including high WC (OR, 0.76), high BP (OR, 0.79), dysglycemia (OR, 0.79), high TG (OR, 0.76), and low HDL-C (OR, 0.83).

Among subjects who did not develop MS, the number of components of MS significantly decreased in the fifth quintile of SVR, compared to the first quintile of SVR (OR, 0.47; 95% CI, 0.24 to 0.92) (Table 3).

DISCUSSION

This study showed that higher SMM%, MFR, and SVR, which are indicative of relative muscle mass, reduced the risk of high BP, dysglycemia, and high TG, in addition to reducing the development of MS.

MS and associated morbidities include dyslipidemia, hypertension, type 2 diabetes mellitus, and nonalcoholic steatohepatitis, which are known as insulin resistance syndrome. Skeletal muscle is the most abundant insulin-sensitive tissue and plays a crucial role in maintenance of systemic glucose metabolism, accounting for 85% of all insulin-mediated glucose utilization [1]. However, adipose tissue expansion is associated with ectopic lipid accumulation in the skeletal muscle, liver, and kidneys, as well as elevation of inflammatory adipokines and reactive oxygen species. These likely contribute to impaired insulin signaling and action in skeletal muscle, resulting in development of MS [5].

Some recent studies have reported an association between skeletal muscle and MS. Londono et al. [9] demonstrated an inverse association between thigh muscle mass and MS and a direct association between chest muscle perimeter and MS. Atlantis et al. [10] also reported that low muscle mass and low strength were the strongest risk factors for MS, independent of abdominal fat, and other factors. However, in this study, the absolute total amount of SMM (i.e., SMM) was positively associated with risk of developing MS, which conflicts with the aforementioned studies. Like SMM, SMI was positively associated with MS. These results may be caused by total BFM, including VFA, which often increases with SMM [11]. In fact, in this study, BFM had a significant positive correlation with SMM and height (data not shown).

Newman et al. [11] compared two different approaches to defining sarcopenic obesity using appendicular lean mass divided by height squared and appendicular lean mass adjusted for both height and BFM. They assessed the relationship between these two definitions of sarcopenic obesity and lower extremity function and other health-related factors. They found that the classification based on both height and fat mass was more strongly associated with lower extremity functional limitations and suggested that fat mass should be considered when estimating sarcopenia in overweight or obese individuals [11]. According to the Korean Longitudinal Study on Health and Aging, the ratio of lower appendicular SMM to weight was more closely associated with MS than either sarcopenia or obesity alone [12]. The Korean Sarcopenic Obesity Study also reported that the muscle to fat ratio (appendicular SMM to VFA), which was defined as a new index of sarcopenic obesity, was negatively associated with MS (OR, 5.43; 95% CI, 2.56 to 13.34) [13,14]. The Third National Health and Nutrition Examination found that the highest quintile of SMI (the ratio of total SMM to total BW) was associated with improved insulin sensitivity and lower risk of transitional/pre- or overt diabetes [15]. Findings in the present study were similar to the aforementioned studies: higher SMM%, MFR, and SVR, which are indicative of relative muscle mass, were found to reduce the risk of high BP, dysglycemia, and high TG, resulting in reduced development of MS. These results suggest that relative SMM ratio to body composition (particularly body fat) is more important than absolute amount of SMM in development of MS.

In addition, among subjects who did not develop MS, the number of components of MS increased in subjects in the lowest quintile of SVR. This suggests that proportion of SMM may play a role in the early progression to MS and could also support the importance of relative SMM in development of MS.

Among the anthropometric parameters, BMI and SVR were associated with all five components of MS. On the other hand, among components of MS, only high WC, which is a fundamental component of MS, was significantly associated with all of the anthropometric parameters.

Although retrospective, this study was a cohort study, and thus showed a more powerful causal relationship between SMM and development of MS in comparison with most previously reported cross-sectional studies. In addition, the effect of SMM on development of MS was investigated using ratio of SMM to weight, total BFM, and VFA. This study showed that the relative ratio of SMM to body composition is important in development of MS.

Anthropometric parameters were estimated using BIA. Computed tomography (CT) and magnetic resonance imaging (MRI) are reference methods for assessment of SMM [16]. Dual-energy X-ray absorptiometry (DXA), which is well correlated with CT and MRI, has been proposed for analysis of body composition, due to its lower cost and higher availability [17-19]. However, it has a limitation in that different densitometers and software versions give different estimates of body composition. In addition, DXA, as well as CT and MRI, cannot be employed for population studies, mainly because of logistical problems [7]. In contrast, BIA offers a simpler and more rapid means of estimating SMM, with less radiation exposure risk than DXA [6,7]. Therefore, it is probably the better candidate for assessment of SMM at the population level.

Modified NCEP-ATP III criteria, rather than international diabetes federation (IDF) criteria, were used for defining MS because it has been reported that central obesity is less prevalent in Koreans than Caucasians and the IDF criteria are inferior to the modified NCEP criteria in identifying high-risk patients who lack central obesity [20].

This study has some limitations. This was a retrospective study and the follow-up period was relatively short. Physical activities that may affect SMM were not taken into account [21,22]. Muscle strength, which is a factor in insulin sensitivity of skeletal muscle, was not taking into account either [10].

In conclusion, decreased SMM may play a critical role in the development of MS and may have some synergic effects on increased visceral fat. Therefore, relative ratio to body composition, not absolute amount of SMM, may be more predictive and important to the development of MS.

NOTES

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

REFERENCES

• 1. Bonora E, Targher G. Increased risk of cardiovascular disease and chronic kidney disease in NAFLD. Nat Rev Gastroenterol Hepatol 2012;9:372-381. ArticlePubMedPDF
• 2. Despres JP. Is visceral obesity the cause of the metabolic syndrome? Ann Med 2006;38:52-63. ArticlePubMed
• 3. DeFronzo RA, Jacot E, Jequier E, Maeder E, Wahren J, Felber JP. The effect of insulin on the disposal of intravenous glucose. Results from indirect calorimetry and hepatic and femoral venous catheterization. Diabetes 1981;30:1000-1007. ArticlePubMedPDF
• 4. Baron AD, Brechtel G, Wallace P, Edelman SV. Rates and tissue sites of non-insulin- and insulin-mediated glucose uptake in humans. Am J Physiol 1988;255(6 Pt 1):E769-E774. ArticlePubMed
• 5. Stump CS, Henriksen EJ, Wei Y, Sowers JR. The metabolic syndrome: role of skeletal muscle metabolism. Ann Med 2006;38:389-402. ArticlePubMed
• 6. Lintsi M, Kaarma H, Kull I. Comparison of hand-to-hand bioimpedance and anthropometry equations versus dual-energy X-ray absorptiometry for the assessment of body fat percentage in 17-18-year-old conscripts. Clin Physiol Funct Imaging 2004;24:85-90. ArticlePubMed
• 7. Malavolti M, Mussi C, Poli M, Fantuzzi AL, Salvioli G, Battistini N, Bedogni G. Cross-calibration of eight-polar bioelectrical impedance analysis versus dual-energy X-ray absorptiometry for the assessment of total and appendicular body composition in healthy subjects aged 21-82 years. Ann Hum Biol 2003;30:380-391. ArticlePubMed
• 8. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). 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;106:3143-3421. ArticlePubMed
• 9. Londono FJ, Calderon JC, Gallo J. Association between thigh muscle development and the metabolic syndrome in adults. Ann Nutr Metab 2012;61:41-46. ArticlePubMedPDF
• 10. Atlantis E, Martin SA, Haren MT, Taylor AW, Wittert GA. Members of the Florey Adelaide Male Ageing Study. Inverse associations between muscle mass, strength, and the metabolic syndrome. Metabolism 2009;58:1013-1022. ArticlePubMed
• 11. Newman AB, Kupelian V, Visser M, Simonsick E, Goodpaster B, Nevitt M, Kritchevsky SB, Tylavsky FA, Rubin SM, Harris TB. Health ABC Study Investigators. Sarcopenia: alternative definitions and associations with lower extremity function. J Am Geriatr Soc 2003;51:1602-1609. ArticlePubMed
• 12. Lim S, Kim JH, Yoon JW, Kang SM, Choi SH, Park YJ, Kim KW, Lim JY, Park KS, Jang HC. Sarcopenic obesity: prevalence and association with metabolic syndrome in the Korean Longitudinal Study on Health and Aging (KLoSHA). Diabetes Care 2010;33:1652-1654. ArticlePubMedPMCPDF
• 13. Lim KI, Yang SJ, Kim TN, Yoo HJ, Kang HJ, Song W, Baik SH, Choi DS, Choi KM. The association between the ratio of visceral fat to thigh muscle area and metabolic syndrome: the Korean Sarcopenic Obesity Study (KSOS). Clin Endocrinol (Oxf) 2010;73:588-594. ArticlePubMed
• 14. Kim TN, Park MS, Lim KI, Yang SJ, Yoo HJ, Kang HJ, Song W, Seo JA, Kim SG, Kim NH, Baik SH, Choi DS, Choi KM. Skeletal muscle mass to visceral fat area ratio is associated with metabolic syndrome and arterial stiffness: The Korean Sarcopenic Obesity Study (KSOS). Diabetes Res Clin Pract 2011;93:285-291. ArticlePubMed
• 15. Srikanthan P, Karlamangla AS. Relative muscle mass is inversely associated with insulin resistance and prediabetes. Findings from the third National Health and Nutrition Examination Survey. J Clin Endocrinol Metab 2011;96:2898-2903. ArticlePubMed
• 16. Roche AF, Heymsfield S, Lohman TG. Human body composition. Chapter 6, Estimation of muscle mass. Champaign: Human Kinetics; 1996. p. 109-128.
• 17. Fuller NJ, Hardingham CR, Graves M, Screaton N, Dixon AK, Ward LC, Elia M. Assessment of limb muscle and adipose tissue by dual-energy X-ray absorptiometry using magnetic resonance imaging for comparison. Int J Obes Relat Metab Disord 1999;23:1295-1302. ArticlePubMedPDF
• 18. Levine JA, Abboud L, Barry M, Reed JE, Sheedy PF, Jensen MD. Measuring leg muscle and fat mass in humans: comparison of CT and dual-energy X-ray absorptiometry. J Appl Physiol (1985) 2000;88:452-456. ArticlePubMed
• 19. Visser M, Fuerst T, Lang T, Salamone L, Harris TB. Validity of fan-beam dual-energy X-ray absorptiometry for measuring fat-free mass and leg muscle mass Health, Aging, and Body Composition Study: Dual-Energy X-ray Absorptiometry and Body Composition Working Group. J Appl Physiol (1985) 1999;87:1513-1520. PubMed
• 20. Yoon YS, Lee ES, Park C, Lee S, Oh SW. The new definition of metabolic syndrome by the international diabetes federation is less likely to identify metabolically abnormal but non-obese individuals than the definition by the revised national cholesterol education program: the Korea NHANES study. Int J Obes (Lond) 2007;31:528-534. ArticlePubMedPDF
• 21. Jung JY, Han KA, Ahn HJ, Kwon HR, Lee JH, Park KS, Min KW. Effects of aerobic exercise intensity on abdominal and thigh adipose tissue and skeletal muscle attenuation in overweight women with type 2 diabetes mellitus. Diabetes Metab J 2012;36:211-221. ArticlePubMedPMC
• 22. Aoi W, Naito Y, Yoshikawa T. Dietary exercise as a novel strategy for the prevention and treatment of metabolic syndrome: effects on skeletal muscle function. J Nutr Metab 2011;2011:676208ArticlePubMedPMCPDF

Fig. 1

Proportions of each quintile in subjects with metabolic syndrome according to various anthropometric parameters. The fifth quintile of percent of skeletal muscle mass (SMM%), skeletal muscle to body fat ratio (MFR), and skeletal muscle to visceral fat ratio (SVR) accounted for the smallest proportion in subjects with metabolic syndrome. BMI, body mass index; VFA, visceral fat area; SMI, skeletal muscle index.

Table 1

Baseline characteristics of all subjects according to development or no development of metabolic syndrome

Values are presented as mean±standard deviation.

BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; SMM%, percent of skeletal muscle mass; SMI, skeletal muscle index; MFR, skeletal muscle to body fat ratio; SVR, skeletal muscle to visceral fat area.

^aP<0.05 for Student t-test between two groups.

Table 2

Odds ratios and 95% confidence intervals for each component of metabolic syndrome and development of metabolic syndrome according to increasing quintiles of each anthropometric parameter

WC, waist circumference; BP, blood pressure; TG, triglyceride; HDL, high density lipoprotein; MS, metabolic syndrome; BMI, body mass index; SMM, skeletal muscle mass; VFA, visceral fat area; SMM%, percent of skeletal muscle mass; SMI, skeletal muscle index; MFR, skeletal muscle to body fat mass; SVR, skeletal muscle to visceral fat area.

^aP<0.05, age- and sex-adjusted odds ratios are presented.

Table 3

Odds ratios and 95% confidence intervals of increase in the number of components of metabolic syndrome in the fifth quintile of each anthropometric parameter reference to the first quintile among subjects who did not develop metabolic syndrome

OR, odds ratio; MS, metabolic syndrome; CI, confidence interval; BMI, body mass index; SMM, skeletal muscle mass; VFA, visceral fat area; SMM%, percent of skeletal muscle mass; SMI, skeletal muscle index; MFR, skeletal muscle to body fat mass; SVR, skeletal muscle to visceral fat area.

^aP<0.05, age- and sex-adjusted ORs are presented.

Figure & Data

References

Citations

Citations to this article as recorded by  

• Relationship between adiponectin and muscle mass in patients with metabolic syndrome and obesity
  Daniel de Luis, David Primo, Olatz Izaola, Juan José Lopez Gomez
  Journal of Diabetes and its Complications.2024; 38(4): 108706.     CrossRef
• The longitudinal association of adipose-to-lean ratio with incident cardiometabolic morbidity: The CARDIA study
  Robert Booker, Mandy Wong, Michael P. Bancks, Mercedes R. Carnethon, Lisa S. Chow, Cora E. Lewis, Pamela J. Schreiner, Shaina J. Alexandria
  Journal of Diabetes and its Complications.2024; 38(5): 108725.     CrossRef
• Assessing Sarcopenic Obesity Risk in Children During the COVID-19 Pandemic: Grip-to-BMI Ratio
  Bahar Öztelcan Gündüz, Aysu Duyan Çamurdan, Mücahit Yıldız, F. Nur Baran Aksakal, Emine Nüket Ünsal
  Medical Research Reports.2024; 7(1): 18.     CrossRef
• Differences in Type 2 Fiber Composition in the Vastus Lateralis and Gluteus Maximus of Patients with Hip Fractures
  Jingwen Tian, Minchul Song, Kyu Jeong Cho, Ho Yeop Lee, Sang Hyeon Ju, Jung Ryul Lim, Ha Thi Nga, Thi Linh Nguyen, Ji Sun Moon, Hyo Ju Jang, Jung-Mo Hwang, Hyon-Seung Yi
  Endocrinology and Metabolism.2024; 39(3): 521.     CrossRef
• Sex differences in body composition in youth with type 1 diabetes and its predictive value in cardiovascular disease risk assessment
  Avivit Brener, Sandy Hamama, Hagar Interator, Asaf Ben Simon, Irina Laurian, Anna Dorfman, Efrat Chorna, Michal Yackobovitch‐Gavan, Asaf Oren, Ori Eyal, Yael Lebenthal
  Diabetes/Metabolism Research and Reviews.2023;[Epub]     CrossRef
• Epidemiological, mechanistic, and practical bases for assessment of cardiorespiratory fitness and muscle status in adults in healthcare settings
  Jaime A. Gallo-Villegas, Juan C. Calderón
  European Journal of Applied Physiology.2023; 123(5): 945.     CrossRef
• The Effect of Childhood Obesity or Sarcopenic Obesity on Metabolic Syndrome Risk in Adolescence: The Ewha Birth and Growth Study
  Hyunjin Park, Seunghee Jun, Hye-Ah Lee, Hae Soon Kim, Young Sun Hong, Hyesook Park
  Metabolites.2023; 13(1): 133.     CrossRef
• Relationships of BMI, muscle-to-fat ratio, and handgrip strength-to-BMI ratio to physical fitness in Spanish children and adolescents
  Samuel Manzano-Carrasco, Jorge Garcia-Unanue, Eero A. Haapala, Jose Luis Felipe, Leonor Gallardo, Jorge Lopez-Fernandez
  European Journal of Pediatrics.2023; 182(5): 2345.     CrossRef
• Body physique rating as a factor to identify at-risk Mexicans for Metabolic Syndrome
  Oscar Herrera-Fomperosa, Sergio K. Bustamante-Villagomez, Sarahí Vazquez-Álvarez, Gabriela Vázquez-Marroquín, Leonardo M. Porchia, Enrique Torres-Rasgado, Ricardo Pérez-Fuentes, M. Elba Gonzalez-Mejia
  Human Nutrition & Metabolism.2023; 33: 200206.     CrossRef
• The association between creatinine to body weight ratio and the risk of progression to diabetes from pre-diabetes: a 5-year cohort study in Chinese adults
  Tong Li, Changchun Cao, Xuan Xuan, Wenjing Liu, Xiaohua Xiao, Cuimei Wei
  BMC Endocrine Disorders.2023;[Epub]     CrossRef
• Muscle-to-Fat Ratio for Predicting Metabolic Syndrome Components in Children with Overweight and Obesity
  Noga Salton, Sharona Kern, Hagar Interator, Adar Lopez, Hadar Moran-Lev, Yael Lebenthal, Avivit Brener
  Childhood Obesity.2022; 18(2): 132.     CrossRef
• Mentale Gesundheit und physische Aktivität
  Wolfgang Laube
  Manuelle Medizin.2022; 60(1): 13.     CrossRef
• Increased odds of having the metabolic syndrome with greater fat‐free mass: counterintuitive results from the National Health and Nutrition Examination Survey database
  Jean‐Christophe Lagacé, Alexis Marcotte‐Chenard, Jasmine Paquin, Dominic Tremblay, Martin Brochu, Isabelle J. Dionne
  Journal of Cachexia, Sarcopenia and Muscle.2022; 13(1): 377.     CrossRef
• Lipoprotein subfractions in patients with sarcopenia and their relevance to skeletal muscle mass and function
  Hui Gong, Yang Liu, Xing Lyu, Lini Dong, Xiangyu Zhang
  Experimental Gerontology.2022; 159: 111668.     CrossRef
• Health Risks of Sarcopenic Obesity in Overweight Children and Adolescents: Data from the CHILT III Programme (Cologne)
  Carolin Sack, Nina Ferrari, David Friesen, Fabiola Haas, Marlen Klaudius, Lisa Schmidt, Gabriel Torbahn, Hagen Wulff, Christine Joisten
  Journal of Clinical Medicine.2022; 11(1): 277.     CrossRef
• Creatinine-to-body weight ratio is a predictor of incident diabetes: a population-based retrospective cohort study
  Jiacheng He
  Diabetology & Metabolic Syndrome.2022;[Epub]     CrossRef
• Relative low muscle mass and muscle strength is associated with the prevalence of metabolic syndrome in patients with type 2 diabetes
  Maya Takegami, Yoshitaka Hashimoto, Masahide Hamaguchi, Ayumi Kaji, Ryosuke Sakai, Takuro Okamura, Noriyuki Kitagawa, Takafumi Osaka, Hiroshi Okada, Naoko Nakanishi, Saori Majima, Takafumi Senmaru, Emi Ushigome, Mai Asano, Masahiro Yamazaki, Michiaki Fuku
  Journal of Clinical Biochemistry and Nutrition.2022; 71(2): 136.     CrossRef
• “Big Data” Approaches for Prevention of the Metabolic Syndrome
  Xinping Jiang, Zhang Yang, Shuai Wang, Shuanglin Deng
  Frontiers in Genetics.2022;[Epub]     CrossRef
• Teil 2: Muskeldysfunktionen – mit Training gegen Schmerz
  Wolfgang Laube
  Manuelle Medizin.2022; 60(3): 129.     CrossRef
• Impact of Low Skeletal Muscle Mass and Obesity on Hearing Loss in Asymptomatic Individuals: A Population-Based Study
  Chul-Hyun Park, Kyung Jae Yoon, Yong-Taek Lee, Sung Min Jin, Sang Hyuk Lee, Tae Hwan Kim
  Healthcare.2022; 10(10): 2022.     CrossRef
• Physical activity level, sitting time, and skeletal muscle mass between esports players and non-esports players
  Zhi H. SEE, Mohamad S. ABDUL HAMID
  Gazzetta Medica Italiana Archivio per le Scienze Mediche.2022;[Epub]     CrossRef
• Associations of changes in fat free mass with risk for type 2 diabetes: Hispanic Community Health Study/Study of Latinos
  M.N. LeCroy, S. Hua, R.C. Kaplan, D. Sotres-Alvarez, Q. Qi, B. Thyagarajan, L.C. Gallo, A. Pirzada, M.L. Daviglus, N. Schneiderman, G.A. Talavera, C.R. Isasi
  Diabetes Research and Clinical Practice.2021; 171: 108557.     CrossRef
• The phospholipase A2 family’s role in metabolic diseases: Focus on skeletal muscle
  Iris Prunonosa Cervera, Brendan M. Gabriel, Peter Aldiss, Nicholas M. Morton
  Physiological Reports.2021;[Epub]     CrossRef
• Bone Health in Aging Men: Does Zinc and Cuprum Level Matter?
  Aleksandra Rył, Tomasz Miazgowski, Aleksandra Szylińska, Agnieszka Turoń-Skrzypińska, Alina Jurewicz, Andrzej Bohatyrewicz, Iwona Rotter
  Biomolecules.2021; 11(2): 237.     CrossRef
• Animal Protein versus Plant Protein in Supporting Lean Mass and Muscle Strength: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
  Meng Thiam Lim, Bernice Jiaqi Pan, Darel Wee Kiat Toh, Clarinda Nataria Sutanto, Jung Eun Kim
  Nutrients.2021; 13(2): 661.     CrossRef
• Effects of Exercise Intervention on Mitochondrial Stress Biomarkers in Metabolic Syndrome Patients: A Randomized Controlled Trial
  Jae Seung Chang, Jun Namkung
  International Journal of Environmental Research and Public Health.2021; 18(5): 2242.     CrossRef
• Association between sarcopenia level and metabolic syndrome
  Su Hwan Kim, Ji Bong Jeong, Jinwoo Kang, Dong-Won Ahn, Ji Won Kim, Byeong Gwan Kim, Kook Lae Lee, Sohee Oh, Soon Ho Yoon, Sang Joon Park, Doo Hee Lee, Masaki Mogi
  PLOS ONE.2021; 16(3): e0248856.     CrossRef
• Sarcopenia and Appendicular Muscle Mass as Predictors of Impaired Fasting Glucose/Type 2 Diabetes in Elderly Women
  Carola Buscemi, Yvelise Ferro, Roberta Pujia, Elisa Mazza, Giada Boragina, Angela Sciacqua, Salvatore Piro, Arturo Pujia, Giorgio Sesti, Silvio Buscemi, Tiziana Montalcini
  Nutrients.2021; 13(6): 1909.     CrossRef
• Low muscle mass in older adults and mortality: A systematic review and meta-analysis
  Felipe M. de Santana, Melissa O. Premaor, Nicolas Y. Tanigava, Rosa M.R. Pereira
  Experimental Gerontology.2021; 152: 111461.     CrossRef
• Association of obesity, visceral adiposity, and sarcopenia with an increased risk of metabolic syndrome: A retrospective study
  Su Hwan Kim, Hyoun Woo Kang, Ji Bong Jeong, Dong Seok Lee, Dong-Won Ahn, Ji Won Kim, Byeong Gwan Kim, Kook Lae Lee, Sohee Oh, Soon Ho Yoon, Sang Joon Park, Mauro Lombardo
  PLOS ONE.2021; 16(8): e0256083.     CrossRef
• Der Muskulatur mehr Aufmerksamkeit schenken!
  Wolfgang Laube
  Manuelle Medizin.2021; 59(4): 302.     CrossRef
• Dietary Protein Requirement Threshold and Micronutrients Profile in Healthy Older Women Based on Relative Skeletal Muscle Mass
  Praval Khanal, Lingxiao He, Hans Degens, Georgina K. Stebbings, Gladys L. Onambele-Pearson, Alun G. Williams, Martine Thomis, Christopher I. Morse
  Nutrients.2021; 13(9): 3076.     CrossRef
• Effects of different definitions of low muscle mass on its association with metabolic syndrome in older adults: A Korean nationwide study
  Yerim Jeon, Ki Young Son
  Geriatrics & Gerontology International.2021; 21(11): 1003.     CrossRef
• Musclin Is Related to Insulin Resistance and Body Composition, but Not to Body Mass Index or Cardiorespiratory Capacity in Adults
  Yeliana L. Sánchez, Manuela Yepes-Calderón, Luis Valbuena, Andrés F. Milán, María C. Trillos-Almanza, Sergio Granados, Miguel Peña, Mauricio Estrada-Castrillón, Juan C. Aristizábal, Raúl Narvez-Sanchez, Jaime Gallo-Villegas, Juan C. Calderón
  Endocrinology and Metabolism.2021; 36(5): 1055.     CrossRef
• Relative Lean Body Mass and Waist Circumference for the Identification of Metabolic Syndrome in the Korean General Population
  Eunjoo Kwon, Eun-Hee Nah, Suyoung Kim, Seon Cho
  International Journal of Environmental Research and Public Health.2021; 18(24): 13186.     CrossRef
• Impact of Skeletal Muscle Mass on Metabolic Health
  Gyuri Kim, Jae Hyeon Kim
  Endocrinology and Metabolism.2020; 35(1): 1.     CrossRef
• Skeletal muscle area and density are associated with lipid and lipoprotein cholesterol levels: The Multi-Ethnic Study of Atherosclerosis
  Chantal A. Vella, Megan C. Nelson, Jonathan T. Unkart, Iva Miljkovic, Matthew A. Allison
  Journal of Clinical Lipidology.2020; 14(1): 143.     CrossRef
• Creatinine/(cystatin C × body weight) ratio is associated with skeletal muscle mass index
  Kensuke Nishida, Yoshitaka Hashimoto, Ayumi Kaji, Takuro Okamura, Ryousuke Sakai, Noriyuki Kitagawa, Takafumi Osaka, Masahide Hamaguchi, Michiaki Fukui
  Endocrine Journal.2020; 67(7): 733.     CrossRef
• Testosterone Therapy for Prevention and Treatment of Obesity in Men
  Monica Caliber, Farid Saad
  Androgens: Clinical Research and Therapeutics.2020; 1(1): 40.     CrossRef
• Creatinine to Body Weight Ratio Is Associated with Incident Diabetes: Population-Based Cohort Study
  Yoshitaka Hashimoto, Takuro Okamura, Masahide Hamaguchi, Akihiro Obora, Takao Kojima, Michiaki Fukui
  Journal of Clinical Medicine.2020; 9(1): 227.     CrossRef
• Skeletal muscle – A bystander or influencer of metabolic syndrome?
  Gina L. Richter-Stretton, Andrew S. Fenning, Rebecca K. Vella
  Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2020; 14(5): 867.     CrossRef
• Using relative handgrip strength to identify children at risk of sarcopenic obesity
  Seryozha Gontarev, Mirko Jakimovski, Georgi Georgiev
  Nutrición Hospitalaria.2020;[Epub]     CrossRef
• Nonalcoholic Fatty Liver Disease Is Associated With Low Skeletal Muscle Mass in Overweight/Obese Youths
  Lucia Pacifico, Francesco Massimo Perla, Gianmarco Andreoli, Rosangela Grieco, Pasquale Pierimarchi, Claudio Chiesa
  Frontiers in Pediatrics.2020;[Epub]     CrossRef
• Independent and combined associations of cardiorespiratory fitness and muscle strength with metabolic syndrome in older adults: A cross-sectional study
  Marcyo Câmara, Rodrigo Alberto Vieira Browne, Gabriel Costa Souto, Daniel Schwade, Ludmila Pereira Lucena Cabral, Geovani Araújo Dantas Macêdo, Luiz Fernando Farias-Junior, Fabíola Leite Gouveia, Telma Maria Araújo Moura Lemos, Kenio Costa Lima, Todd A. D
  Experimental Gerontology.2020; 135: 110923.     CrossRef
• A counterintuitive perspective for the role of fat‐free mass in metabolic health
  Jean‐Christophe Lagacé, Martin Brochu, Isabelle J. Dionne
  Journal of Cachexia, Sarcopenia and Muscle.2020; 11(2): 343.     CrossRef
• Response: The way fat‐free mass is reported may change the conclusions regarding its protective effect on metabolic health
  Jean‐Christophe Lagace, Martin Brochu, Isabelle J. Dionne
  Clinical Endocrinology.2020; 92(1): 79.     CrossRef
• The Association between Major Dietary Pattern and Low Muscle Mass in Korean Middle-Aged and Elderly Populations: Based on the Korea National Health and Nutrition Examination Survey
  Seong-Ah Kim, Jinwoo Ha, Byeonghwi Lim, Jun-Mo Kim, Sangah Shin
  Nutrients.2020; 12(11): 3543.     CrossRef
• Total body skeletal muscle mass and diet in children aged 6–8 years: ANIVA Study
  Maria Morales-Suarez-Varela, Isabel Peraita-Costa, Carlos Guillamon Escudero, Agustin Llopis-Morales, Agustin Llopis-Gonzalez
  Applied Physiology, Nutrition, and Metabolism.2019; 44(9): 944.     CrossRef
• The way fat‐free mass is reported may change the conclusions regarding its protective effect on metabolic health
  Jean‐Christophe Lagacé, Dominic Tremblay, Jasmine Paquin, Alexis Marcotte‐Chénard, Martin Brochu, Isabelle J. Dionne
  Clinical Endocrinology.2019; 91(6): 903.     CrossRef
• Components of Metabolic Syndrome in Korean Adults: A Hospital-Based Cohort at Seoul National University Bundang Hospital
  Soo Lim, Se Hee Min, Ji Hyun Lee, Lee Kyung Kim, Dong-Hwa Lee, Jie-Eun Lee, Kyoung Min Kim, Sunmi Lee, Kyoung-Chan Park, Yun Jong Lee
  Journal of Obesity & Metabolic Syndrome.2019; 28(2): 118.     CrossRef
• Skeletal muscle as a protagonist in the pregnancy metabolic syndrome
  Raul Narvaez-Sanchez, Juan C. Calderón, Gloria Vega, Maria Camila Trillos, Sara Ospina
  Medical Hypotheses.2019; 126: 26.     CrossRef
• Increase in relative skeletal muscle mass over time and its inverse association with metabolic syndrome development: a 7-year retrospective cohort study
  Gyuri Kim, Seung-Eun Lee, Ji Eun Jun, You-Bin Lee, Jiyeon Ahn, Ji Cheol Bae, Sang-Man Jin, Kyu Yeon Hur, Jae Hwan Jee, Moon-Kyu Lee, Jae Hyeon Kim
  Cardiovascular Diabetology.2018;[Epub]     CrossRef
• Association of Age-Related Trends in Blood Pressure and Body Composition Indices in Healthy Adults
  Wei Li, Yan He, Lili Xia, Xinghua Yang, Feng Liu, Jingang Ma, Zhiping Hu, Yajun Li, Dongxue Li, Jiajia Jiang, Guangliang Shan, Changlong Li
  Frontiers in Physiology.2018;[Epub]     CrossRef
• Metabolic syndrome and body shape predict differences in health parameters in farm working women
  Ilze Mentoor, Maritza Kruger, Theo Nell
  BMC Public Health.2018;[Epub]     CrossRef
• Associations of stunting in early childhood with cardiometabolic risk factors in adulthood
  Emanuella De Lucia Rolfe, Giovanny Vinícius Araújo de França, Carolina Avila Vianna, Denise P. Gigante, J. Jaime Miranda, John S. Yudkin, Bernardo Lessa Horta, Ken K. Ong, C. Mary Schooling
  PLOS ONE.2018; 13(4): e0192196.     CrossRef
• Relationship Between Relative Skeletal Muscle Mass and Nonalcoholic Fatty Liver Disease: A 7‐Year Longitudinal Study
  Gyuri Kim, Seung‐Eun Lee, You‐Bin Lee, Ji Eun Jun, Jiyeon Ahn, Ji Cheol Bae, Sang‐Man Jin, Kyu Yeon Hur, Jae Hwan Jee, Moon‐Kyu Lee, Jae Hyeon Kim
  Hepatology.2018; 68(5): 1755.     CrossRef
• Relationships among Skeletal Muscle Mass, Health Related Factors, Nutrient Intake, and Physical Activities in Male Adolescents: Based on the 5th (2009-2011) Korean National Health and Nutrition Examination Survey (KNHANES)
  In-Kyung Jung, Jung-Hyun Kim
  The Korean Journal of Community Living Science.2018; 29(2): 185.     CrossRef
• Using relative handgrip strength to identify children at risk of sarcopenic obesity
  Michal Steffl, Jan Chrudimsky, James J. Tufano, Masaki Mogi
  PLOS ONE.2017; 12(5): e0177006.     CrossRef
• International society of sports nutrition position stand: diets and body composition
  Alan A. Aragon, Brad J. Schoenfeld, Robert Wildman, Susan Kleiner, Trisha VanDusseldorp, Lem Taylor, Conrad P. Earnest, Paul J. Arciero, Colin Wilborn, Douglas S. Kalman, Jeffrey R. Stout, Darryn S. Willoughby, Bill Campbell, Shawn M. Arent, Laurent Banno
  Journal of the International Society of Sports Nutrition.2017;[Epub]     CrossRef
• Relationship between rectus abdominis muscle thickness and metabolic syndrome in middle-aged men
  Eun Sil Choi, Soo Hyun Cho, Jung-Ha Kim, Etsuro Ito
  PLOS ONE.2017; 12(9): e0185040.     CrossRef
• Health-related quality of life and activity limitation in an elderly Korean population with sarcopenia: The Fourth Korea National Health and Nutrition Examination Survey (KNHANES IV-2, 3), 2008–2009
  T.H. Kim, S.-H. Kim, J. Kim, H.-J. Hwang
  European Geriatric Medicine.2017; 8(4): 360.     CrossRef
• Differential association between sarcopenia and metabolic phenotype in Korean young and older adults with and without obesity
  You‐Cheol Hwang, In‐Jin Cho, In‐Kyung Jeong, Kyu Jeung Ahn, Ho Yeon Chung
  Obesity.2017; 25(1): 244.     CrossRef
• Reference Values of Skeletal Muscle Mass for Korean Children and Adolescents Using Data from the Korean National Health and Nutrition Examination Survey 2009-2011
  Kirang Kim, Sangmo Hong, Eun Young Kim, Bin He
  PLOS ONE.2016; 11(4): e0153383.     CrossRef
• Insulin sensitivity, body composition and adipose depots following 12 w combined endurance and strength training in dysglycemic and normoglycemic sedentary men
  Torgrim Mikal Langleite, Jørgen Jensen, Frode Norheim, Hanne Løvdal Gulseth, Daniel Steensen Tangen, Kristoffer Jensen Kolnes, Ansgar Heck, Tryggve Storås, Guro Grøthe, Marius Adler Dahl, Anders Kielland, Torgeir Holen, Hans Jørgen Noreng, Hans Kristian S
  Archives of Physiology and Biochemistry.2016; 122(4): 167.     CrossRef
• Association between fat free mass and glucose homeostasis: Common knowledge revisited
  Karine Perreault, Jean-Christophe Lagacé, Martin Brochu, Isabelle J. Dionne
  Ageing Research Reviews.2016; 28: 46.     CrossRef
• Low skeletal muscle mass is associated with non-alcoholic fatty liver disease in Korean adults: the Fifth Korea National Health and Nutrition Examination Survey
  Hee Yeon Kim, Chang Wook Kim, Chung-Hwa Park, Jong Young Choi, Kyungdo Han, Anwar T Merchant, Yong-Moon Park
  Hepatobiliary & Pancreatic Diseases International.2016; 15(1): 39.     CrossRef
• The ratio of skeletal muscle mass to visceral fat area is a main determinant linking circulating irisin to metabolic phenotype
  You-Cheol Hwang, Won Seon Jeon, Cheol-Young Park, Byung-Soo Youn
  Cardiovascular Diabetology.2016;[Epub]     CrossRef
• Effects of a weight loss program on body composition and the metabolic profile in obese postmenopausal women displaying various obesity phenotypes: a MONET group study
  Eve Normandin, Eric Doucet, Rémi Rabasa-Lhoret, Martin Brochu
  Applied Physiology, Nutrition, and Metabolism.2015; 40(7): 695.     CrossRef
• Analytic morphomics identifies predictors of new‐onset diabetes after liver transplantation
  Valerie M. Vaughn, David C. Cron, Michael N. Terjimanian, Zachary S. Gala, Stewart C. Wang, Grace L. Su, Michael L. Volk
  Clinical Transplantation.2015; 29(5): 458.     CrossRef
• Reduced Flexibility Associated with Metabolic Syndrome in Community-Dwelling Elders
  Ke-Vin Chang, Chen-Yu Hung, Chia-Ming Li, Yu-Hung Lin, Tyng-Guey Wang, Keh-Sung Tsai, Der-Sheng Han, Diego Fraidenraich
  PLOS ONE.2015; 10(1): e0117167.     CrossRef
• Metabolic risk factors in U.S. youth with low relative muscle mass
  Sunkyung Kim, Rodolfo Valdez
  Obesity Research & Clinical Practice.2015; 9(2): 125.     CrossRef
• Comparison of waist to height ratio and body indices for prediction of metabolic disturbances in the Korean population: the Korean National Health and Nutrition Examination Survey 2008–2011
  Seok Hui Kang, Kyu Hyang Cho, Jong Won Park, Jun Young Do
  BMC Endocrine Disorders.2015;[Epub]     CrossRef
• N-3 fatty acid intake altered fat content and fatty acid distribution in chicken breast muscle, but did not influence mRNA expression of lipid-related enzymes
  Anna Haug, Nicole F Nyquist, Magny Thomassen, Arne T Høstmark, Tone-Kari Knutsdatter Østbye
  Lipids in Health and Disease.2014;[Epub]     CrossRef
• Métodos de análise da composição corporal em adultos obesos
  Rávila Graziany Machado de Souza, Aline Corado Gomes, Carla Marques Maia do Prado, João Felipe Mota
  Revista de Nutrição.2014; 27(5): 569.     CrossRef

About this article

Park BS, Yoon JS. Relative Skeletal Muscle Mass Is Associated with Development of Metabolic Syndrome. Diabetes Metab J. 2013;37(6):458-464.

Received: Aug 21, 2013; Accepted: Sep 27, 2013

DOI: https://doi.org/10.4093/dmj.2013.37.6.458.

Download citation ↓