INTRODUCTION

The number of patients in Japan with obesity due to insufficient exercise and overeating has increased in recent years. Based on insulin resistance, obesity may cause metabolic syndrome, which is related to development of hypertension, abnormal lipid metabolism, and diabetes, and increases the risk of arteriosclerosis, ischemic cardiac disease, and cerebrovascular disease. Thus, obesity requires appropriate treatment and prevention [12]. Obese patients have accumulation of visceral fat and enlarged fat cells [3], and these cells can lead to insulin resistance and may cause various diseases due to vascular endothelial function disorder [456]. Therefore, the main goals of obesity treatment are to decrease fat and improve insulin resistance, mainly through intervention with aerobic exercise [7].

It has recently been suggested that skeletal muscle also has an important role in the cause of insulin resistance [24]. The exercise/metabolism function and oxygen consumption of skeletal muscles are dependent on muscle cells and mitochondria [89]. Furthermore, skeletal muscles are target tissues of insulin and have endocrine function to produce cytokines called myokines. Thus, a decrease in skeletal muscle mass may be a factor in insulin resistance [1011], and maintenance and increase of the skeletal muscle mass might be useful for improvement of insulin resistance. However, the details of this process are still unclear. Therefore, we examined the relationship between changes in skeletal muscle mass in obese patients who reduced body weight and insulin resistance measured before and after the decrease in body weight.

METHODS

Subjects and study protocol

Medical records were reviewed for 213 females who underwent our obesity program to reduce body weight. Of these patients, 52 aged over 60 years old were excluded. Of the remaining 161 patients, 111 and 50 (including 10 who subsequently dropped out) had reductions of body weight of >5% and <5%, respectively. After this evaluation, 19 patients with diabetes under treatment with insulin or oral diabetes drugs, extremely severe obesity (body mass index [BMI] ≥46 kg/m²), or significantly decreased skeletal muscle index (SMI <5.45 kg/m²) were excluded. A retrospective study was performed in 92 patients with obesity with BMI ≥30 kg/m². There were some differences between patients with reductions of body weight of >5% and <5%. However, we want to emphasize the important factors for insulin resistance, and therefore we do not show data for subjects with body weight reduction <5%.

Based on the ethical code of Kansai Medical University, this study was performed after providing an explanation of the objectives, details and notes to all subjects, and obtaining written informed consent for participation in the study.

Body weight control program and monitoring

The body weight control program included exercise therapy, monthly nutritional guidance, and psychological counseling. A symptom-limited cardiopulmonary exercise (CPX) was performed before the program was used to determine the anaerobic threshold oxygen uptake (ATVO₂) and peak oxygen uptake (peak VO₂) in each subject. The exercise program lasted for 70 minutes, and consisted of 30 minutes of aerobic exercise, such as that on a bicycle or treadmill, and gravity level resistance exercise of stretching [12]. Over a period of 6 months, exercise therapy was supervised by a health exercise instructor at least once or twice a month in our health science center. The subjects were asked to perform similar exercise three times a week at home. For exercise at home, a pedometer was given to each subject to measure individual activity. The individual exercise amounts were determined from the pedometer at every visit to the center. The health exercise instructor also provided guidance to subjects at each visit. For nutritional guidance, education on eating behavior and dietary instruction was mainly provided. The dietitian mainly provided advice on eating behavior based on cognitive behavioral medicine. In psychological counseling, a clinical psychotherapist provided guidance, with a focus on self-monitoring and self-efficacy, based on cognitive behavioral therapy [1314].

Examinations

Physical findings (height and body weight), body composition, and blood tests in a fasting state were measured prior to and after the 6-month intervention. Blood was collected after overnight fasting. Serum insulin was measured by chemiluminescent immunoassay (Elecsys Insulin Assay; Roche Diagnostics Ltd., Tokyo, Japan) and determined using an automatic electrochemiluminescence immunoassay analyzer (Modular Analytics EE; Roche Diagnostics). The homeostasis model assessment of insulin resistance (HOMA-IR) was used to estimate insulin resistance, using fasting blood insulin in the early morning (IRI) and the fasting blood sugar (FBS) levels, based on the equation: HOMA-IR=(IRI×FBS)/405 [15]. This equation is most appropriate for evaluation of insulin resistance in patients without diabetes.

Cardiopulmonary exercise

A symptom-limited CPX was performed by all patients using a bicycle ergometer (232C-XL; Combi Co. Ltd., Tokyo, Japan). In analysis of expired gas, the measurement was performed with the breath-by-breath method using an expired gas analyzer (AE-300 system; Minato Medical Science Co. Ltd., Osaka, Japan). AT was determined using the V-slope method. peak VO₂ was defined as the highest level under load.

Body composition and skeletal muscle mass indexes

Body fat and fat-free mass were measured using dual-energy X-ray absorptiometry (DXA; DPX-NT system; General Electric, Fairfield, CT, USA). After the 6-month intervention, these items were determined before other effects were evaluated. Using absolute values of fat-free mass (whole body, upper extremities, body trunk, and lower extremities) from DXA. The SMI was obtained by adjusting the skeletal muscle mass for height (skeletal muscle mass of extremities/height² [kg/m²]), the appendicular skeletal muscle (ASM) index (/body weight) was obtained by adjusting the ASM mass for body weight (skeletal muscle mass of extremities/body weight [%]), and the % skeletal muscle was obtained by adjusting the total fat-free mass for body weight (total fat-free mass/body weight [%]).

Statistical analysis

All data are expressed as mean±standard deviation. Individual values obtained before and after the intervention were compared by paired t-test and Wilcoxon signed-rank test. For comparison between groups, an independent t-test and Mann-Whitney U test were used. Pearson correlation and Spearman rank-correlation coefficients were used to examine the relationship of changes in individual values. Stepwise multiple linear regression analysis was used for multivariate analysis. All calculations were performed using SPSS version 21 (IBM Co., Armonk, NY, USA). Differences at P<0.05 were considered significant in all analyses.

RESULTS

Characteristics

A retrospective study was performed in 92 patients with obesity with BMI ≥30 kg/m². The average age, body weight and BMI were 40.9±10.4 years, 82.9±11.8 kg, and 33.2±4.6 kg/m², respectively (Table 1).

Changes caused by the exercise intervention body weight control program

The background of the patients is shown in Table 1. After the 6-month body weight control program, all subjects had a body weight decrease of ≥5% and the amounts of fat and fat-free mass were also significantly reduced. The body weight change from before to after intervention was –10.3%±4.5% (82.9±11.8 to 74.2±10.6 kg, P<0.01), the decrease in total fat mass was –19.3%±9.6% (38.9±8.2 to 31.4±7.9 kg, P<0.01), the decrease in total fat-free mass was –2.7%±4.3% (41.4±5.2 to 40.1±4.4 kg, P<0.01), and the decrease in HOMA-IR was –20.2%±38.3% (3.3±2.7 to 2.3±1.6, P<0.01).

For skeletal muscle mass, SMI significantly decreased by 2.3%±1.6% (7.4±0.9 to 7.1±0.7 kg, P<0.01), ASM index (/body weight) significantly increased by 8.9%±8.0% (22.4±2.4 to 24.3±2.6 kg, P<0.01). % Body fat significantly decreased by –10.1%±7.5%, and % skeletal muscle significantly increased by 8.9%±7.2% (50.2±4.5 to 54.6±5.4 kg, P<0.01). Thus, all these parameters changed significantly from before to after the intervention.

In exercise tolerance, ATVO₂ significantly increased by 9.1%±18.4% (11.3±2.1 to 12.1±2.0 mL/kg/min), and peak VO₂ significantly increased by 11.0%±14.2% (19.8±3.8 to 21.7±4.1 mL/kg/min) (Table 2).

Relationship between the change in HOMA-IR and changes in body composition

In the 92 subjects, the change in HOMA-IR was significantly positively correlated with changes in body weight (r=0.256, P<0.05), BMI (r=0.251, P<0.05), amount of fat (whole body: r=0.258, P<0.05; body trunk: r=0.210, P<0.05), and % body fat (r=0.228, P<0.05), and significantly negatively correlated with the change in % skeletal muscle mass (r=–0.280, P<0.01). In stepwise multiple linear regression analysis of these parameters and age as independent variables and the change in HOMA-IR as the dependent variable, a change in % skeletal muscle was identified as an independent predictor of HOMA-IR (β=–0.280, R²=0.068, P<0.01) (Table 3).

DISCUSSION

Female patients with obesity who completed a 6-month body weight control program and successfully reduced their body weight were selected as subjects of this study, to identify changes in body composition factors that are associated with improvement of insulin resistance. Comparison of data obtained before and after the intervention showed that % skeletal muscle was maintained, while body weight was significantly decreased by ≥5%, fat mass was reduced, and exercise tolerance and insulin resistance improved. The program mainly comprised aerobic exercise, and active resistance training for increase of skeletal muscle was not performed. Therefore, there were no cases in this study whose skeletal muscle mass was significantly decreased more than 5% instead of fat mass reduction.

A decrease in SMI occurred from before to after the intervention. Since sex hormones are highly correlated with maintenance of skeletal muscle [16], only female patients aged ≤60 years were included in the study. In obesity treatment, since total body weight is reduced in body weight control, a certain decrease in skeletal muscles is physiologically appropriate. In this study, fat-free mass and SMI significantly decreased after the intervention, while ASM index (/body weight) and % skeletal muscle significantly increased. These parameters reflect the amount of skeletal muscle adjusted for height, body weight, and total fat-free mass [17]. Nomura and Ouchi [7] found that body composition tends to vary more with aging, and suggested the importance of the amount of visceral fat and the skeletal muscle mass in obesity treatment. Therefore in this study, we propose that the % skeletal muscle can be used for appropriate evaluation of skeletal muscle in body composition after obese patients reduce their body weight.

Fat is thought to be the most important factor in insulin resistance, and obesity with a large amount of visceral fat has a particularly strong correlation with insulin resistance [181920]. We did not examine visceral fat in this study, but Yamanaka et al. [21] has shown relationships of insulin resistance with an increase in fat and the characteristics of the fat. In addition, enlarged fat cells have decreased production of adiponectin, which facilitates insulin sensitivity in skeletal muscles, and increased production of harmful adipocytokines. Such harmful cytokines include tumor necrosis factor α and resistin, and with free fatty acids these cytokines induce insulin resistance by inhibiting insulin signaling in skeletal muscle and the liver [456].

In contrast, in this study, the most important factor for insulin resistance in female patients with obesity was the % skeletal muscle, rather than exercise tolerance or the fat index. This finding is consistent with recent reports suggesting that tissue changes in skeletal muscles are strongly involved in insulin resistance. The motor/metabolism function and oxygen consumption of skeletal muscles are promoted by a close mitochondria network on the surface of the myocyte membrane or myofibril septum [89]. If the amount or function of such mitochondria is decreased, fatty acids in skeletal muscles are not metabolized and accumulate as diacylglycerol, long-chain acyl-coenzyme A, and ceramide, which may aggravate insulin resistance by inhibiting insulin signaling, including a decrease of glucose transporter type 4 (GLUT4) follicles [356]. Furthermore, skeletal muscles are target tissues for insulin and have endocrine function to produce myokines [2223]. A decrease in skeletal muscle mass induces a decrease of mitochondria, and thus fatty acids in skeletal muscle are not metabolized, resulting in insulin resistance [1011]. This suggests that a decrease of fat and maintenance or an increase in % skeletal muscle can improve insulin resistance and contribute to a favorable outcome of obesity treatment.

In stepwise multiple linear regression analysis in this study, the coefficient for the relationship of the change in % skeletal muscle with improvement of insulin resistance was only 0.068. This low value may be due to use of HOMA-IR as the index of insulin resistance, since this value mainly reflects insulin resistance in the liver [24] and might be largely influenced by visceral fat [23]. No relationships with other skeletal muscle mass indexes were found in this analysis. In 2012, Kim et al. [25] compared the validities of diagnoses using extremity SMIs adjusted by height and body weight in patients and found that sarcopenia and sarcopenia with obesity were under evaluated using SMI (adjusted for height). Moon et al. [10] used data adjusted for body weight to investigate the role of skeletal muscle in development of non-alcoholic fatty liver disease [2627]. When evaluating muscle-related conditions of obese subjects, their whole body size should be considered. The % skeletal muscle might be the most appropriate index for skeletal muscle in obese patients with large changes in body weight, since no other indexes were significant in this study. Our results also suggest that maintenance of skeletal muscle is more important than increased exercise tolerance in obese female patients who reduce their body weight.

There were several limitations in the study. First, we did not evaluate menopausal stage, which is a concern because sex hormones are correlated with body composition. However, there are a few studies of important factors, other than menopause, in women under 60 years [2829]. Lee et al. [30] also found that such subjects had significant negative correlations of HOMA-IR with ASM and ASM/height², after adjustment for body weight. Therefore subjects over 60 years old were excluded and we consider that the effects of menopause were excluded to a certain extent in this study. Second, although the subjects were obese patients with BMI ≥30 kg/m², adiponectin produced from fat tissues was not measured. We mainly examined body composition in regard to insulin resistance, but further studies on nutritional factors, such as proteins and amino acids, that may affect skeletal muscles are also required. In addition, male subjects and wider BMI and age groups should be included in future studies.

In conclusion, significant decreases in body weight and % body fat were found in female patients with obesity who completed a body weight control program. These patients also had increased exercise tolerance and % skeletal muscle. We suggest that % skeletal muscle is a useful index that is related to a change in HOMA-IR. This finding indicates that skeletal muscles contribute to improvement of HOMA-IR in female patients with obesity.

The present study indicated that maintaining skeletal muscle mass during reducing body weight in female patients with obesity may be the essential of success for a body weight management. Further, this results suggested that the independent factor of the % skeletal muscle was solely HOMA-IR. An adequate parameter for muscle-related conditions of obese patients may be the % skeletal muscle.

Acknowledgements

ACKNOWLEDGMENTS

We would like to thank the subjects who participated in the study. We express our gratitude for the assistance of staff members of the Health Science Center of Kansai Medical University. Parts of this study were reported at the 35th Annual Meeting of the Japan Society for the Study of Obesity (Miyazaki, October 2014).

NOTES

CONFLICTS OF INTEREST: No potential conflict of interest relevant to this article was reported.

REFERENCES

• 1. Ministry of Health, Labour and Welfare. The National Health and Nutrition Survey in Japan 2012. Tokyo: Ministry of Health, Labour and Welfare; 2012. p. 181.
• 2. Committee on Obesity Disease Management Guidelines of the Japan Society for the Study of Obesity: guidelines for the management of obesity disease 2006. J Jpn Soc Study Obes 2006;12:10-15.
• 3. Kawanaka K. Effects of exercise on glucose metabolism in skeletal muscle. J Jpn Soc Anti Aging Med 2011;7:25-31.
• 4. Kadowaki T. Insights into insulin resistance and type 2 diabetes from knockout mouse models. J Clin Invest 2000;106:459-465. ArticlePubMedPMC
• 5. Okuno A, Tamemoto H, Tobe K, Ueki K, Mori Y, Iwamoto K, Umesono K, Akanuma Y, Fujiwara T, Horikoshi H, Yazaki Y, Kadowaki T. Troglitazone increases the number of small adipocytes without the change of white adipose tissue mass in obese Zucker rats. J Clin Invest 1998;101:1354-1361. ArticlePubMedPMC
• 6. Yamauchi T, Kamon J, Waki H, Murakami K, Motojima K, Komeda K, Ide T, Kubota N, Terauchi Y, Tobe K, Miki H, Tsuchida A, Akanuma Y, Nagai R, Kimura S, Kadowaki T. The mechanisms by which both heterozygous peroxisome proliferator-activated receptor gamma (PPARgamma) deficiency and PPARgamma agonist improve insulin resistance. J Biol Chem 2001;276:41245-41254. PubMed
• 7. Nomura K, Ouchi Y. Obesity in the elderly. J Jpn Soc Study Obes 2011;17:201-209.
• 8. Huang JH, Hood DA. Age-associated mitochondrial dysfunction in skeletal muscle: contributing factors and suggestions for long-term interventions. IUBMB Life 2009;61:201-214. ArticlePubMed
• 9. Petersen KF, Befroy D, Dufour S, Dziura J, Ariyan C, Rothman DL, DiPietro L, Cline GW, Shulman GI. Mitochondrial dysfunction in the elderly: possible role in insulin resistance. Science 2003;300:1140-1142. ArticlePubMedPMC
• 10. Moon JS, Yoon JS, Won KC, Lee HW. The role of skeletal muscle in development of nonalcoholic fatty liver disease. Diabetes Metab J 2013;37:278-285. ArticlePubMedPMC
• 11. Lee CG, Boyko EJ, Strotmeyer ES, Lewis CE, Cawthon PM, Hoffman AR, Everson-Rose SA, Barrett-Connor E, Orwoll ES. Osteoporotic Fractures in Men Study Research Group. Association between insulin resistance and lean mass loss and fat mass gain in older men without diabetes mellitus. J Am Geriatr Soc 2011;59:1217-1224. ArticlePubMedPMC
• 12. Kurose S, Tsutsumi H, Yamanaka Y, Shinno H, Miyauchi T, Tamanoi A, Imai M, Masuda I, Kimura Y. Improvement in endothelial function by lifestyle modification focused on exercise training is associated with insulin resistance in obese patients. Obes Res Clin Pract 2014;8:e106-e114. ArticlePubMed
• 13. Kimura Y. Dietary instruction and cognitive behavioral therapy. J Clin Sports Med 2009;26:225-230.
• 14. Saito H, Kimura Y, Tashima S, Takao N, Nakagawa A, Baba T, Sato S. Psychological factors that promote behavior modification by obese patients. Biopsychosoc Med 2009;3:9ArticlePubMedPMC
• 15. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-419. ArticlePubMedPDF
• 16. Aloia JF, Vaswani A, Russo L, Sheehan M, Flaster E. The influence of menopause and hormonal replacement therapy on body cell mass and body fat mass. Am J Obstet Gynecol 1995;172:896-900. ArticlePubMed
• 17. Kohara K. Sarcopenic obesity. Jpn J Geriatr 2014;51:99-108.
• 18. Ishibashi S. Aging and insulin resistance. Med Frontline 2011;66:818-822.
• 19. Coon PJ, Rogus EM, Drinkwater D, Muller DC, Goldberg AP. Role of body fat distribution in the decline in insulin sensitivity and glucose tolerance with age. J Clin Endocrinol Metab 1992;75:1125-1132. ArticlePubMed
• 20. Kohrt WM, Kirwan JP, Staten MA, Bourey RE, King DS, Holloszy JO. Insulin resistance in aging is related to abdominal obesity. Diabetes 1993;42:273-281. ArticlePubMed
• 21. Yamanaka Y, Shinno H, Kurose S, Tsutsumi H, Kimura Y. Trial evaluation of visceral fat characteristics by abdominal bioelectrical impedance method. J Diabetes Metab 2013;4:292.
• 22. Pedersen BK, Steensberg A, Fischer C, Keller C, Keller P, Plomgaard P, Febbraio M, Saltin B. Searching for the exercise factor: is IL-6 a candidate? J Muscle Res Cell Motil 2003;24:113-119. PubMed
• 23. Pedersen BK. Muscle as a secretory organ. Compr Physiol 2013;3:1337-1362. ArticlePubMedPDF
• 24. Furugen M, Saitoh S, Ohnishi H, Akasaka H, Mitsumata K, Chiba M, Furukawa T, Miyazaki Y, Shimamoto K, Miura T. Matsuda-DeFronzo insulin sensitivity index is a better predictor than HOMA-IR of hypertension in Japanese: the Tanno-Sobetsu study. J Hum Hypertens 2012;26:325-333. ArticlePubMedPDF
• 25. Kim YS, Lee Y, Chung YS, Lee DJ, Joo NS, Hong D, Song G, Kim HJ, Choi YJ, Kim KM. Prevalence of sarcopenia and sarcopenic obesity in the Korean population based on the Fourth Korean National Health and Nutritional Examination Surveys. J Gerontol A Biol Sci Med Sci 2012;67:1107-1113. ArticlePubMed
• 26. Wing RR, Matthews KA, Kuller LH, Meilahn EN, Plantinga PL. Weight gain at the time of menopause. Arch Intern Med 1991;151:97-102. ArticlePubMed
• 27. Dawson-Hughes B, Harris S. Regional changes in body composition by time of year in healthy postmenopausal women. Am J Clin Nutr 1992;56:307-313. ArticlePubMed
• 28. Schuster J, Beninca G, Vitorazzi R, Morelo Dal Bosco S. Effects of oats on lipid profile, insulin resistance and weight loss. Nutr Hosp 2015;32:2111-2116. PubMed
• 29. Lilja M, Rolandsson O, Shaw JE, Pauvaday V, Cameron AJ, Tuomilehto J, Alberti KG, Zimmet PZ, Soderberg S. Higher leptin levels in Asian Indians than Creoles and Europids: a potential explanation for increased metabolic risk. Int J Obes (Lond) 2010;34:878-885. ArticlePubMedPDF
• 30. Lee SW, Youm Y, Lee WJ, Choi W, Chu SH, Park YR, Kim HC. Appendicular skeletal muscle mass and insulin resistance in an elderly Korean population: the Korean social life, health and aging project-health examination cohort. Diabetes Metab J 2015;39:37-45. ArticlePubMedPMC

Table 1

Background of the patients

Variable	Value
Age, yr	40.9±10.4
Anthropometrics
Body weight, kg	82.9±11.8
Body mass index, kg/m2	33.2±4.6
% Body fat, %	46.6±4.6
Smoking status
Never	82 (89.1)
Current	2 (2.2)
Past	8 (8.7)
Drinking status
Never	82 (89.1)
Current	1 (1.1)
Past	9 (9.8)
Drug information
Diabetes	0
Hypertension	8 (8.7)
Hyperlipidemia	2 (2.2)

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

Table 2

Changes in physiological, anthropometric, and exercise capacity parameters and HOMA-IR before and after the program

Variable	Baseline	After program	P value
Body weight, kg	82.9±11.8	74.2±10.6	<0.01
Body mass index, kg/m2	33.2±4.6	29.7±4.2	<0.01
Skeletal muscle index, kg/m2	7.4±0.9	7.1±0.7	<0.01
Appendicular skeletal muscle index, weight %	22.4±2.4	24.3±2.6	<0.01
Total lean mass, kg	41.4±5.2	40.1±4.4	<0.01
Lower extremity lean mass	14.1±2.0	13.8±1.7	<0.01
Trunk lean mass	19.4±2.9	18.8±2.3	<0.01
Total fat mass, kg	38.9±8.2	31.4±7.9	<0.01
Lower extremity fat mass	11.8±3.3	9.5±3.0	<0.01
Trunk fat mass	22.1±4.8	18.0±4.6	<0.01
% Skeletal muscle, %	50.2±4.5	54.6±5.4	<0.01
% Body fat, %	46.6±4.6	41.9±5.6	<0.01
Exercise capacity, mL/kg/min
ATVO2	11.3±2.1	12.1±2.0	<0.01
Peak VO2	19.8±3.8	21.7±4.1	<0.01
Metabolic parameters
Fasting glucose, mg/dL	99.0±12.9	94.7±8.1	<0.01
Fasting insulin, µU/mL	13.4±9.8	9.9±6.6	<0.01
HOMA-IR (×10–6 mol×IU×L–2)	3.3±2.7	2.3±1.6	<0.01
ALT	35.3±31.4	18.2±10.4	<0.01
AST	27.4±20.7	19.2±12.1	<0.01
Triglycerides, mg/dL	131.6±80.9	101.1±59.2	<0.01
HDL-C, mg/dL	47.2±10.1	52.6±9.1	<0.01
LDL-C, mg/dL	129.0±32.5	125.2±32.6	0.141

Values are presented as mean±standard deviation.

HOMA-IR, homeostasis model assessment of insulin resistance; ATVO₂, anaerobic threshold oxygen uptake; Peak VO₂, peak oxygen uptake; ALT, alanine transaminas; AST, aspartate aminotransferase; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol.

Table 3

Stepwise multiple linear regression analysis for evaluation of the associations between HOMA-IR and the other parameters in the obese group

Dependent variable	Independent variable	β	P value
Δ HOMA-IR	Δ % Skeletal muscle	–0.280	<0.01
	Δ Body weight	0.109	0.463
	Δ BMI	0.106	0.458
	Δ Trunk fat mass	–0.140	0.499
	Δ Total fat mass	–0.033	0.910
	Δ % Body fat	–0.299	0.312
	Δ ATVO2	–0.117	0.260
	Δ Peak VO2	–0.056	0.613
	Age	0.026	0.800

HOMA-IR, homeostasis model assessment of insulin resistance; BMI, body mass index; ATVO₂, anaerobic threshold oxygen uptake; Peak VO₂, peak oxygen uptake.

Figure & Data

References

Citations

Citations to this article as recorded by  

• Liver lipid metabolism, oxidative stress, and inflammation in glutamine-supplemented ob/ob mice
  Jaqueline Santos Moreira Leite, Eloisa Aparecida Vilas-Boas, Hilton K. Takahashi, Ana Cláudia Munhoz, Layanne C.C. Araújo, Carla Roberta Carvalho, Jose Donato Jr, Rui Curi, Angelo Rafael Carpinelli, Vinicius Cruzat
  The Journal of Nutritional Biochemistry.2025; 138: 109842.     CrossRef
• Consequences of Dietary Manganese-Based Nanoparticles Supplementation or Deficiency on Systemic Health and Gut Metabolic Dynamics in Rats
  Przemysław Sołek, Karolina Różaniecka, Jerzy Juśkiewicz, Bartosz Fotschki, Anna Stępniowska, Katarzyna Ognik
  Nanotechnology, Science and Applications.2025; Volume 18: 19.     CrossRef
• Decreased muscle percentage and increased fat percentage associated with obstructive sleep apnea syndrome severity in males: Insight from body composition and polysomnogram
  Yinghui Gao, Libo Zhao, Li Fan, Weimeng Cai, Dong Rui, Zhe Zhao, Tingyu Nie, Tianjiao Li, Xin Xue, Cong Ma, Hongju Xiao, Lin Liu
  Heliyon.2025; 11(6): e43131.     CrossRef
• Effects of a multicomponent intervention on body composition and cardiovascular risk factors in women with obesity– a randomized clinical trial
  Leyla Esmealy, Babak Esmealy, Farhad Vahid, Javad Vakili
  Journal of Diabetes & Metabolic Disorders.2025;[Epub]     CrossRef
• Sarcopenia prevalence between obese and morbid obese patients in an obesity center
  Mujgan Tuna, Arzu Cennet Işık , Ozlem Hürmeydan
  Journal of Surgery and Medicine.2024; 8(4): 73.     CrossRef
• Association between insulin sensitivity and lean mass loss during weight loss
  Ciera L. Bartholomew, Catia Martins, Barbara Gower
  Obesity.2024; 32(6): 1156.     CrossRef
• Dietary behaviour change intervention for managing sarcopenic obesity among community-dwelling older people: a pilot randomised controlled trial
  Yue-Heng Yin, Justina Yat Wa Liu, Maritta Välimäki
  BMC Geriatrics.2023;[Epub]     CrossRef
• Diet and Exercise in the Management of Polycystic Ovary Syndrome: Practical Considerations for Person-Centered Care
  Giorgia E. Colombo, Stephanie Pirotta, Angelo Sabag
  Seminars in Reproductive Medicine.2023; 41(01/02): 026.     CrossRef
• Effect of Mobility Restrictions During the Coronavirus Disease Epidemic on Body Composition and Exercise Tolerance in Patients With Obesity: Single Institutional Retrospective Cohort Study
  Yoshinari Matsumoto, Satoshi Kurose, Takumi Miyauchi, Sawako Yoshiuchi, Daiki Habu, Yutaka Kimura
  Journal of Physical Activity and Health.2022; 19(5): 351.     CrossRef
• Interrelationship of Gut Microbiota, Obesity, Body Composition and Insulin Resistance in Asians with Type 2 Diabetes Mellitus
  Che-Sheng Pai, Cheng-Yuan Wang, Wei-Wen Hung, Wei-Chun Hung, Hui-Ju Tsai, Chen-Chia Chang, Shang-Jyh Hwang, Chia-Yen Dai, Wen-Yu Ho, Yi-Chun Tsai
  Journal of Personalized Medicine.2022; 12(4): 617.     CrossRef
• Exercise and Nutrition Strategies for Combating Sarcopenia and Type 2 Diabetes Mellitus in Older Adults
  Dionysia Argyropoulou, Nikolaos D. Geladas, Tzortzis Nomikos, Vassilis Paschalis
  Journal of Functional Morphology and Kinesiology.2022; 7(2): 48.     CrossRef
• Branched-Chain and Aromatic Amino Acids in Relation to Fat Mass and Fat-Free Mass Changes among Adolescents: A School-Based Intervention
  Magnoudewa Priscille Pana, Pierre Ayotte, Elhadji Anassour-Laouan-Sidi, Edouard Suhas, Clémence Mahana Iti Gatti, Michel Lucas
  Metabolites.2022; 12(7): 589.     CrossRef
• Exercising for Insulin Sensitivity – Is There a Mechanistic Relationship With Quantitative Changes in Skeletal Muscle Mass?
  Jasmine Paquin, Jean-Christophe Lagacé, Martin Brochu, Isabelle J. Dionne
  Frontiers in Physiology.2021;[Epub]     CrossRef
• The relationship between changes in serum myostatin and adiponectin levels in patients with obesity undergoing a weight loss program
  Nana Takao, Satoshi Kurose, Takumi Miyauchi, Katsuko Onishi, Atsuko Tamanoi, Ryota Tsuyuguchi, Aya Fujii, Sawako Yoshiuchi, Kazuhisa Takahashi, Hiromi Tsutsumi, Yutaka Kimura
  BMC Endocrine Disorders.2021;[Epub]     CrossRef
• Exercise induces favorable metabolic changes in white adipose tissue preventing high‐fat diet obesity
  Babu R. Maharjan, Sergio F. Martinez‐Huenchullan, Susan V. Mclennan, Stephen M. Twigg, Paul F. Williams
  Physiological Reports.2021;[Epub]     CrossRef
• THE RELATIONSHIP BETWEEN INSULIN RESISTANCE AND FA-TIGUE SYMPTOM IN FIBROMYALGIA SYNDROME
  Türkan Turgay, Zekiye İpek Katirci Kirmaci, Pınar Günel Karadeniz, Mehmet Baştemir
  International Journal of Research -GRANTHAALAYAH.2020; 8(3): 271.     CrossRef
• Epicardial adipose tissue is tightly associated with exercise intolerance in patients with type 2 diabetes mellitus with asymptomatic left ventricular structural and functional abnormalities
  Yousuke Sugita, Katsuhiko Ito, Shigeki Sakurai, Satoshi Sakai, Shinya Kuno
  Journal of Diabetes and its Complications.2020; 34(5): 107552.     CrossRef
• Dose Frequency Optimization of the Dual Amylin and Calcitonin Receptor Agonist KBP-088: Long-Lasting Improvement in Food Preference and Body Weight Loss
  Anna Thorsø Larsen, Nina Sonne, Kim V. Andreassen, Morten A. Karsdal, Kim Henriksen
  The Journal of Pharmacology and Experimental Therapeutics.2020; 373(2): 269.     CrossRef
• Healthy dietary pattern and their corresponding gut microbiota profile are linked to a lower risk of type 2 diabetes, independent of the presence of obesity
  D.A. Díaz-Rizzolo, B. Kostov, M. López-Siles, A. Serra, C. Colungo, L. González-de-Paz, M. Martinez-Medina, A. Sisó-Almirall, R. Gomis
  Clinical Nutrition.2020; 39(2): 524.     CrossRef

• Changes in Body Composition and FTO Whole Blood DNA Methylation Among Japanese Women: A Randomized Clinical Trial of Weight-Loss Program

  
  Haruhiko Nishida, Katsuko Onishi, Satoshi Kurose, Hiromi Tsutsumi, Takumi Miyauchi, Nana Takao, Sawako Yoshiuchi, Aya Fujii, Yutaka Kimura
  Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2020; Volume 13: 2157.     CrossRef
• Effects of low skeletal muscle mass and sarcopenic obesity on albuminuria: a 7-year longitudinal study
  Jee Hee Yoo, Gyuri Kim, Sung Woon Park, Min Sun Choi, Jiyeon Ahn, Sang-Man Jin, Kyu Yeon Hur, Moon-Kyu Lee, Mira Kang, Jae Hyeon Kim
  Scientific Reports.2020;[Epub]     CrossRef
• Effects of body weight loss program on parameters of muscle performance in female obese adults
  Gian Pietro Emerenziani, Dafne Ferrari, Silvia Migliaccio, Andrea Lenzi, Emanuela A. Greco, Chiara Marocco, Carlo Baldari, Laura Guidetti
  The Journal of Sports Medicine and Physical Fitness.2019;[Epub]     CrossRef
• Reduced Lean Body Mass and Cardiometabolic Diseases in Adult Males with Overweight and Obesity: A Pilot Study
  Shirine Khazem, Leila Itani, Dima Kreidieh, Dana El Masri, Hana Tannir, Roberto Citarella, Marwan El Ghoch
  International Journal of Environmental Research and Public Health.2018; 15(12): 2754.     CrossRef
• Physical performance measures in screening for reduced lean body mass in adult females with obesity
  M. El Ghoch, A.P. Rossi, S. Calugi, S. Rubele, F. Soave, M. Zamboni, E. Chignola, G. Mazzali, P.V. Bazzani, R. Dalle Grave
  Nutrition, Metabolism and Cardiovascular Diseases.2018; 28(9): 917.     CrossRef
• Testosterone a key factor in gender related metabolic syndrome
  V. E. Bianchi, V. Locatelli
  Obesity Reviews.2018; 19(4): 557.     CrossRef
• Burly1 is a mouse QTL for lean body mass that maps to a 0.8-Mb region of chromosome 2
  Cailu Lin, Brad D. Fesi, Michael Marquis, Natalia P. Bosak, Anna Lysenko, Mohammed Amin Koshnevisan, Fujiko F. Duke, Maria L. Theodorides, Theodore M. Nelson, Amanda H. McDaniel, Mauricio Avigdor, Charles J. Arayata, Lauren Shaw, Alexander A. Bachmanov, D
  Mammalian Genome.2018; 29(5-6): 325.     CrossRef
• Hypoxic Training Improves Normoxic Glucose Tolerance in Adolescents with Obesity
  ESTELLE DE GROOTE, FLORIAN A. BRITTO, LOÏC BULLOCK, MARIE FRANÇOIS, CARINE DE BUCK, HENRI NIELENS, LOUISE DELDICQUE
  Medicine & Science in Sports & Exercise.2018; 50(11): 2200.     CrossRef
• Lipid modulation of skeletal muscle mass and function
  Christopher Lipina, Harinder S Hundal
  Journal of Cachexia, Sarcopenia and Muscle.2017; 8(2): 190.     CrossRef
• Angiotensin-Converting Enzyme Ins/Del Polymorphism and Body Composition: The Intermediary Role of Hydration Status
  Laura Bordoni, Valerio Napolioni, Francesca Marchegiani, Emilio Amadio, Rosita Gabbianelli
  Lifestyle Genomics.2017; 10(1-2): 1.     CrossRef
• Influence of segmental body composition and adiposity hormones on resting metabolic rate and substrate utilization in overweight and obese adults
  K. R. Hirsch, A. E. Smith-Ryan, M. N. M. Blue, M. G. Mock, E. T. Trexler
  Journal of Endocrinological Investigation.2017; 40(6): 635.     CrossRef
• Carbohydrate-Restriction with High-Intensity Interval Training: An Optimal Combination for Treating Metabolic Diseases?
  Monique E. Francois, Jenna B. Gillen, Jonathan P. Little
  Frontiers in Nutrition.2017;[Epub]     CrossRef
• Mediterranean diet and mortality risk in metabolically healthy obese and metabolically unhealthy obese phenotypes
  Y-M Park, S E Steck, T T Fung, J Zhang, L J Hazlett, K Han, A T Merchant
  International Journal of Obesity.2016; 40(10): 1541.     CrossRef
• Effects of Body Weight Reduction on Serum Irisin and Metabolic Parameters in Obese Subjects
  Yaeko Fukushima, Satoshi Kurose, Hiromi Shinno, Ha Cao Thi Thu, Nana Takao, Hiromi Tsutsumi, Takaaki Hasegawa, Toshiaki Nakajima, Yutaka Kimura
  Diabetes & Metabolism Journal.2016; 40(5): 386.     CrossRef

About this article

Fukushima Y, Kurose S, Shinno H, Cao Thu H, Takao N, Tsutsumi H, Kimura Y. Importance of Lean Muscle Maintenance to Improve Insulin Resistance by Body Weight Reduction in Female Patients with Obesity. Diabetes Metab J. 2016;40(2):147-153.

Received: Nov 11, 2015; Accepted: Jan 25, 2016

DOI: https://doi.org/10.4093/dmj.2016.40.2.147.

Download citation ↓