Diabetes & Metabolism Journal

Original Articles

Metabolic Risk/Epidemiology
Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis: Ji Yoon Kim, Jimi Choi, Chantal A. Vella, Michael H. Criqui, Matthew A. Allison, Nam Hoon Kim; Diabetes Metab J. 2022;46(5):747-755. Published online March 30, 2022; DOI: https://doi.org/10.4093/dmj.2021.0294

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Background
The weight-adjusted waist index (WWI) reflected body compositional changes with aging. This study was to investigate the association of WWI with abdominal fat and muscle mass in a diverse race/ethnic population.
Methods
Computed tomography (CT) data from 1,946 participants for abdominal fat and muscle areas from the Multi-Ethnic Study of Atherosclerosis (785 Whites, 252 Asians, 406 African American, and 503 Hispanics) were used. Among them, 595 participants underwent repeated CT. The WWI was calculated as waist circumference (cm) divided by the square root of body weight (kg). The associations of WWI with abdominal fat and muscle measures were examined, and longitudinal changes in abdominal composition measures were compared.
Results
In all race/ethnic groups, WWI was positively correlated with total abdominal fat area (TFA), subcutaneous fat area, and visceral fat area, but negatively correlated with total abdominal muscle area (TMA) and abdominal muscle radiodensity (P<0.001 for all). WWI showed a linear increase with aging regardless of race and there were no significant differences in the WWI distribution between Whites, Asians, and African Americans. In longitudinal analyses, over 38.6 months of follow-up, all abdominal fat measures increased but muscle measures decreased, along with increase in WWI. The more the WWI increased, the more the TFA increased and the more the TMA decreased.
Conclusion
WWI showed positive associations with abdominal fat mass and negative associations with abdominal muscle mass, which likely reflects the abdominal compositional changes with aging in a multi-ethnic population.

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Metabolic Risk/Epidemiology
Maternal Hyperglycemia during Pregnancy Increases Adiposity of Offspring: Hye Rim Chung, Joon Ho Moon, Jung Sub Lim, Young Ah Lee, Choong Ho Shin, Joon-Seok Hong, Soo Heon Kwak, Sung Hee Choi, Hak Chul Jang; Diabetes Metab J. 2021;45(5):730-738. Published online February 22, 2021; DOI: https://doi.org/10.4093/dmj.2020.0154

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Background
The effect of intrauterine hyperglycemia on fat mass and regional fat proportion of the offspring of mothers with gestational diabetes mellitus (OGDM) remains to be determined.
Methods
The body composition of OGDM (n=25) and offspring of normoglycemic mothers (n=49) was compared using dualenergy X-ray absorptiometry at age 5 years. The relationship between maternal glucose concentration during a 100 g oral glucose tolerance test (OGTT) and regional fat mass or proportion was analyzed after adjusting for maternal prepregnancy body mass index (BMI).
Results
BMI was comparable between OGDM and control (median, 16.0 kg/m² vs. 16.1 kg/m² ). Total, truncal, and leg fat mass were higher in OGDM compared with control (3,769 g vs. 2,245 g, P=0.004; 1,289 g vs. 870 g, P=0.017; 1,638 g vs. 961 g, P=0.002, respectively), whereas total lean mass was lower in OGDM (15,688 g vs. 16,941 g, P=0.001). Among OGDM, total and truncal fat mass were correlated with fasting and 3-hour glucose concentrations of maternal 100 g OGTT during pregnancy (total fat mass, r=0.49, P=0.018 [fasting], r=0.473, P=0.023 [3-hour]; truncal fat mass, r=0.571, P=0.004 [fasting], r=0.558, P=0.006 [3-hour]), but there was no correlation between OGDM leg fat mass and maternal OGTT during pregnancy. Regional fat indices were not correlated with concurrent maternal 75 g OGTT values.
Conclusion
Intrauterine hyperglycemia is associated with increased fat mass, especially truncal fat, in OGDM aged 5 years.

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Advances in free fatty acid profiles in gestational diabetes mellitus
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Journal of Translational Medicine.2024;[Epub] CrossRef
High-fat diet during pregnancy lowers fetal weight and has a long-lasting adverse effect on brown adipose tissue in the offspring
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Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms
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Journal of Korean Medical Science.2022;[Epub] CrossRef

Metabolic Risk/Epidemiology
Age- and Sex-Related Differential Associations between Body Composition and Diabetes Mellitus: Eun Roh, Soon Young Hwang, Jung A Kim, You-Bin Lee, So-hyeon Hong, Nam Hoon Kim, Ji A Seo, Sin Gon Kim, Nan Hee Kim, Kyung Mook Choi, Sei Hyun Baik, Hye Jin Yoo; Diabetes Metab J. 2021;45(2):183-194. Published online June 16, 2020; DOI: https://doi.org/10.4093/dmj.2019.0171

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Background

The age- and sex-related differences on the impacts of body composition on diabetes mellitus (DM) remain uncertain.

Methods

The fourth and fifth Korea National Health and Nutrition Examination Survey included 15,586 subjects over 30 years of age who completed dual-energy X-ray absorptiometry. We conducted a cross-sectional study to investigate whether muscle mass index (MMI), defined as appendicular skeletal muscle divided by body mass index (BMI), and fat mass index (FMI), defined as trunk fat mass divided by BMI, were differently associated with DM according to age and sex.

Results

In multivariate logistic regression, the risk for DM significantly increased across quartiles of FMI in men aged ≥70. Meanwhile, MMI showed a protective association with DM in men of the same age. The odds ratios (ORs) for the highest quartile versus the lowest quartile of FMI and MMI were 3.116 (95% confidence interval [CI], 1.405 to 6.914) and 0.295 (95% CI, 0.157 to 0.554), respectively. In women, the ORs of DM was significantly different across FMI quartiles in those over age 50. The highest quartile of FMI exhibited increased ORs of DM in subjects aged 50 to 69 (OR, 1.891; 95% CI, 1.229 to 2.908) and ≥70 (OR, 2.275; 95% CI, 1.103 to 4.69) compared to lowest quartile. However, MMI was not significantly associated with DM in women of all age groups.

Conclusion

Both FMI and MMI were independent risk factors for DM in men aged 70 years or more. In women over 50 years, FMI was independently associated with DM. There was no significant association between MMI and DM in women.

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Research Progress on Correlation between Body Composition Changes and Disease Pro-gression of Type 2 Diabetes
敏张
Advances in Clinical Medicine.2024; 14(03): 936. CrossRef
Low Skeletal Muscle Mass Accompanied by Abdominal Obesity Additively Increases the Risk of Incident Type 2 Diabetes
Ji Eun Jun, Seung-Eun Lee, You-Bin Lee, Gyuri Kim, Sang-Man Jin, Jae Hwan Jee, Jae Hyeon Kim
The Journal of Clinical Endocrinology & Metabolism.2023; 108(5): 1173. CrossRef
Is imaging-based muscle quantity associated with risk of diabetes? A meta-analysis of cohort studies
Shanhu Qiu, Xue Cai, Yang Yuan, Bo Xie, Zilin Sun, Tongzhi Wu
Diabetes Research and Clinical Practice.2022; 189: 109939. CrossRef
Whole and segmental body composition changes during mid-follicular and mid-luteal phases of the menstrual cycle in recreationally active young women
Şükran Nazan Koşar, Yasemin Güzel, Mehmet Gören Köse, Ayşe Kin İşler, Tahir Hazır
Annals of Human Biology.2022; 49(2): 124. CrossRef
Body Composition and Diabetes
Hye Jin Yoo
The Journal of Korean Diabetes.2021; 22(4): 238. CrossRef

Metabolic Risk/Epidemiology
Association between the Thigh Muscle and Insulin Resistance According to Body Mass Index in Middle-Aged Korean Adults: Ji Eun Heo, Jee-Seon Shim, Hokyou Lee, Hyeon Chang Kim; Diabetes Metab J. 2020;44(3):446-457. Published online April 16, 2020; DOI: https://doi.org/10.4093/dmj.2019.0110

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Background

We examined the associations between thigh muscle area (TMA) and insulin resistance (IR) according to body mass index (BMI) in middle-aged Korean general population.

Methods

TMA was measured using quantitative computed tomography and corrected by body weight (TMA/Wt) in 1,263 men, 788 premenopausal women, and 1,476 postmenopausal women all aged 30 to 64 years. The tertiles of TMA/Wt were calculated separately for men and for premenopausal and postmenopausal women. Homeostatic model assessment for insulin resistance (HOMA-IR) was performed using fasting blood glucose and insulin levels, and increased IR was defined according to sex-specific, top quartiles of HOMA-IR. Associations between the TMA/Wt tertiles and increased IR according to the BMI categories (<25 and ≥25 kg/m²) were assessed using multivariable logistic regression analysis.

Results

In men with higher BMIs, but not in those with lower BMIs, the presence of an increased IR had significantly higher odds ratios in the lower TMA/Wt tertiles, even after adjustment for visceral fat area. However, in premenopausal and postmenopausal women, there was no significant inverse association between TMA/Wt tertiles and increased IR, regardless of BMI category.

Conclusion

Our findings suggest that the thigh muscle is inversely associated with IR in men, particularly in those with higher BMIs.

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Non-Alcoholic Fatty Liver Disease with Sarcopenia and Carotid Plaque Progression Risk in Patients with Type 2 Diabetes Mellitus
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Jana S. Schmelzer, Melanie Kahle-Stephan, Juris J. Meier, Michael A. Nauck
Experimental and Clinical Endocrinology & Diabetes.2023; 131(10): 539. CrossRef
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Da Hye Lee, Sung-Chan Kang, Seung-Sik Hwang, Yun Jeong Lee, Hwa Young Kim, Seong Yong Lee, Choong Ho Shin, Jaehyun Kim
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Obesity and Metabolic Syndrome
The Association between Z-Score of Log-Transformed A Body Shape Index and Cardiovascular Disease in Korea: Wankyo Chung, Jung Hwan Park, Hye Soo Chung, Jae Myung Yu, Shinje Moon, Dong Sun Kim; Diabetes Metab J. 2019;43(5):675-682. Published online April 26, 2019; DOI: https://doi.org/10.4093/dmj.2018.0169

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Background

In order to overcome the limitations of body mass index (BMI) and waist circumference (WC), the z-score of the log-transformed A Body Shape Index (LBSIZ) has recently been introduced. In this study, we analyzed the relationship between the LBSIZ and cardiovascular disease (CVD) in a Korean representative sample.

Methods

Data were collected from the Korea National Health and Nutrition Examination VI to V. The association between CVD and obesity indices was analyzed using a receiver operating characteristic curve. The cut-off value for the LBSIZ was estimated using the Youden index, and the odds ratio (OR) for CVD was determined via multivariate logistic regression analysis. ORs according to the LBSIZ value were analyzed using restricted cubic spline regression plots.

Results

A total of 31,227 Korean healthy adults were analyzed. Area under the curve (AUC) of LBSIZ against CVD was 0.686 (95% confidence interval [CI], 0.671 to 0.702), which was significantly higher than the AUC of BMI (0.583; 95% CI, 0.567 to 0.599) or WC (0.646; 95% CI, 0.631 to 0.661) (P<0.001). Similar results were observed for stroke and coronary artery diseases. The cut-off value for the LBSIZ was 0.35 (sensitivity, 64.5%; specificity, 64%; OR, 1.29, 95% CI, 1.12 to 1.49). Under restricted cubic spline regression, LBSIZ demonstrated that OR started to increase past the median value.

Conclusion

The findings of this study suggest that the LBSIZ might be more strongly associated with CVD risks compared to BMI or WC. These outcomes would be helpful for CVD risk assessment in clinical settings, especially the cut-off value of the LBSIZ suggested in this study.

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Association between body shape index and risk of mortality in the United States
Heysoo Lee, Hye Soo Chung, Yoon Jung Kim, Min Kyu Choi, Yong Kyun Roh, Wankyo Chung, Jae Myung Yu, Chang-Myung Oh, Shinje Moon
Scientific Reports.2022;[Epub] CrossRef
Utility of the Z-score of log-transformed A Body Shape Index (LBSIZ) in the assessment for sarcopenic obesity and cardiovascular disease risk in the United States
Wankyo Chung, Jung Hwan Park, Hye Soo Chung, Jae Myung Yu, Dong Sun Kim, Shinje Moon
Scientific Reports.2019;[Epub] CrossRef

Epidemiology
Longitudinal Changes of Body Composition Phenotypes and Their Association with Incident Type 2 Diabetes Mellitus during a 5-Year Follow-up in Koreans: Hong-Kyu Kim, Min Jung Lee, Eun-Hee Kim, Sung-Jin Bae, Jaewon Choe, Chul-Hee Kim, Joong-Yeol Park; Diabetes Metab J. 2019;43(5):627-639. Published online April 19, 2019; DOI: https://doi.org/10.4093/dmj.2018.0141

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Background

To elucidate longitudinal changes of complex body composition phenotypes and their association with incident type 2 diabetes mellitus.

Methods

A total of 17,280 (mean age, 48.1±8.2 years) Korean adults who underwent medical check-ups were included. The mean follow-up duration was 5.5±0.5 years. Body compositions were assessed using a bioelectrical impedance analysis. Four body composition phenotypes were defined using the median of appendicular skeletal muscle mass (ASM) index and fat mass index: low muscle/low fat (LM/LF); high muscle (HM)/LF; LM/high fat (HF); and HM/HF groups.

Results

Of the individuals in the LM/LF or HM/HF groups, over 60% remained in the same group, and over 30% were moved to the LM/HF group. Most of the LM/HF group remained in this group. In the baseline HM/LF group, approximately 30% stayed in the group, and the remaining individuals transitioned to the three other groups in similar proportions. Incident diabetes was significantly lower in participants who remained in the HM/LF group than those who transitioned to the LM/LF or LM/HF group from the baseline HM/LF group in men. ASM index was significantly associated with a decreased risk for incident diabetes in men regardless of obesity status (adjusted odds ratio [OR], 0.71 per kg/m²; 95% confidence interval [CI], 0.52 to 0.97 in non-obese) (adjusted OR, 0.87; 95% CI, 0.77 to 0.98 in obese) after adjusting for other strong risk factors (e.g., baseline glycosylated hemoglobin and homeostasis model assessment of insulin resistance).

Conclusion

Maintenance of ASM may be protective against the development of type 2 diabetes mellitus in men, regardless of obesity status.

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Hong-Kyu Kim, Sung-Jin Bae, Min Jung Lee, Eun Hee Kim, Hana Park, Hwi Seung Kim, Yun Kyung Cho, Chang Hee Jung, Woo Je Lee, Jaewon Choe
Clinical and Molecular Hepatology.2023; 29(4): 987. CrossRef
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Diabetes/Metabolism Research and Reviews.2022;[Epub] CrossRef
Association between fat mass index, fat‐free mass index and hemoglobin A1c in a Japanese population: The Tohoku Medical Megabank Community‐based Cohort Study
Masato Takase, Tomohiro Nakamura, Takumi Hirata, Naho Tsuchiya, Mana Kogure, Fumi Itabashi, Naoki Nakaya, Yohei Hamanaka, Junichi Sugawara, Kichiya Suzuki, Nobuo Fuse, Akira Uruno, Eiichi N Kodama, Shinichi Kuriyama, Ichiro Tsuji, Shigeo Kure, Atsushi Hoz
Journal of Diabetes Investigation.2022; 13(5): 858. CrossRef
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Jee Hee Yoo, Sung Woon Park, Ji Eun Jun, Sang‐Man Jin, Kyu Yeon Hur, Moon‐Kyu Lee, Mira Kang, Gyuri Kim, Jae Hyeon Kim
Diabetes/Metabolism Research and Reviews.2021;[Epub] CrossRef
Reference Data and T-Scores of Lumbar Skeletal Muscle Area and Its Skeletal Muscle Indices Measured by CT Scan in a Healthy Korean Population
Eun Hee Kim, Kyung Won Kim, Yongbin Shin, Jiwoo Lee, Yousun Ko, Ye-Jee Kim, Min Jung Lee, Sung-Jin Bae, Sung Won Park, Jaewon Choe, Hong-Kyu Kim, Anne Newman
The Journals of Gerontology: Series A.2021; 76(2): 265. CrossRef
Age- and Sex-Related Differential Associations between Body Composition and Diabetes Mellitus
Eun Roh, Soon Young Hwang, Jung A Kim, You-Bin Lee, So-hyeon Hong, Nam Hoon Kim, Ji A Seo, Sin Gon Kim, Nan Hee Kim, Kyung Mook Choi, Sei Hyun Baik, Hye Jin Yoo
Diabetes & Metabolism Journal.2021; 45(2): 183. CrossRef
Cardiometabolic Profile of Different Body Composition Phenotypes in Children
Yi Ying Ong, Jonathan Y Huang, Navin Michael, Suresh Anand Sadananthan, Wen Lun Yuan, Ling-Wei Chen, Neerja Karnani, S Sendhil Velan, Marielle V Fortier, Kok Hian Tan, Peter D Gluckman, Fabian Yap, Yap-Seng Chong, Keith M Godfrey, Mary F-F Chong, Shiao-Yn
The Journal of Clinical Endocrinology & Metabolism.2021; 106(5): e2015. CrossRef
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Hong-Kyu Kim, Kyung Won Kim, Eun Hee Kim, Min Jung Lee, Sung-Jin Bae, Yousun Ko, Taeyoung Park, Yongbin Shin, Ye-Jee Kim, Jaewon Choe
Clinical Nutrition.2021; 40(6): 4022. CrossRef
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Hong‐Kyu Kim, Min Jung Lee, Eun Hee Kim, Sung‐Jin Bae, Kyung Won Kim, Chul‐Hee Kim
Obesity.2021; 29(8): 1375. CrossRef
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Melanie S. Haines, Laura E. Dichtel, Kate Santoso, Martin Torriani, Karen K. Miller, Miriam A. Bredella
International Journal of Obesity.2020; 44(9): 1851. CrossRef
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Jin Hwa Kim, Young Sang Lyu, Sang Yong Kim
International Journal of Environmental Research and Public Health.2020; 17(12): 4383. CrossRef
The relationships between sarcopenic skeletal muscle loss during ageing and macronutrient metabolism, obesity and onset of diabetes
Ailsa A. Welch, Richard P. G. Hayhoe, Donnie Cameron
Proceedings of the Nutrition Society.2020; 79(1): 158. CrossRef
Impact of the Dynamic Change of Metabolic Health Status on the Incident Type 2 Diabetes: A Nationwide Population-Based Cohort Study
Jung A Kim, Da Hye Kim, Seon Mee Kim, Yong Gyu Park, Nan Hee Kim, Sei Hyun Baik, Kyung Mook Choi, Kyungdo Han, Hye Jin Yoo
Endocrinology and Metabolism.2019; 34(4): 406. CrossRef

Obesity and Metabolic Syndrome
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: Jung A Kim, Soon Young Hwang, Hye Soo Chung, Nam Hoon Kim, Ji A Seo, Sin Gon Kim, Nan Hee Kim, Kyung Mook Choi, Sei Hyun Baik, Hye Jin Yoo; Diabetes Metab J. 2019;43(1):105-113. Published online September 28, 2018; DOI: https://doi.org/10.4093/dmj.2018.0036

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Background

Sarcopenic obesity (SO) is a serious public health concern, few studies have examined the clinical implications of SO in newly-diagnosed type 2 diabetes mellitus (T2DM) patients. We evaluated the prevalence of the newly diagnosed, drug-naïve T2DM patients with low muscle mass with abdominal obesity and its association with insulin resistance and other diabetic complications.

Methods

We classified 233 drug-naïve T2DM subjects into four groups according to abdominal obesity (waist circumference ≥90 cm in men and ≥85 cm in women) and low muscle mass status (appendicular skeletal muscle <7.0 kg/m² for men and <5.4 kg/m² for women).

Results

The proportion of the subjects with low muscle mass and abdominal obesity among the newly diagnosed, drug-naïve T2DM patients was 8.2%. Homeostasis model assessment of insulin resistance (HOMA-IR) increased linearly according to body composition group from normal to abdominal obesity to both low muscle mass and abdominal obesity. The multiple logistic regression analysis indicated that subjects with low muscle mass and abdominal obesity (odds ratio [OR], 9.39; 95% confidence interval [CI], 2.41 to 36.56) showed a higher risk for insulin resistance, defined as HOMA-IR ≥3, than those with abdominal obesity (OR, 5.36; 95% CI, 2.46 to 11.69), even after adjusting for other covariates. However, there were no differences in lipid profiles, microalbuminuria, or various surrogate markers for atherosclerosis among the four groups.

Conclusion

Subjects with both low muscle mass and abdominal obesity had a higher risk of insulin resistance than those with low muscle mass or abdominal obesity only.

Citations

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Clinical observation on acupuncture for 80 patients with abdominal obesity in Germany: based on the theory of unblocking and regulating the Belt Vessel
Yuanyuan Li, Hang Xiong, Shuhui Ma, Jingzhang Dai
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Waist circumference and end‐stage renal disease based on glycaemic status: National Health Insurance Service data 2009–2018
Yun Kyung Cho, Ji Hye Huh, Shinje Moon, Yoon Jung Kim, Yang‐Hyun Kim, Kyung‐do Han, Jun Goo Kang, Seong Jin Lee, Sung‐Hee Ihm
Journal of Cachexia, Sarcopenia and Muscle.2023; 14(1): 585. CrossRef
Incidence of sarcopenic obesity in older patients with diabetes and association between sarcopenic obesity and higher-level functional capacity: evaluation based on a consensus statement
Satoshi Ida, Ryutaro Kaneko, Kanako Imataka, Kaoru Okubo, Kentaro Azuma, Kazuya Murata
Endocrine Journal.2023; 70(6): 591. CrossRef
A Novel Anthropometric Parameter, Weight-Adjusted Waist Index Represents Sarcopenic Obesity in Newly Diagnosed Type 2 Diabetes Mellitus
Min Jeong Park, Soon Young Hwang, Nam Hoon Kim, Sin Gon Kim, Kyung Mook Choi, Sei Hyun Baik, Hye Jin Yoo
Journal of Obesity & Metabolic Syndrome.2023; 32(2): 130. CrossRef
Prevalence of sarcopenic obesity in patients with diabetes and adverse outcomes: A systematic review and meta-analysis
Yuan-yuan Zhou, Jin-feng Wang, Qian Yao, Qiu-feng Jian, Zhi-peng Luo
Clinical Nutrition ESPEN.2023; 58: 128. CrossRef
The Correlation Between Leg Muscle Mass Index and Non-Alcoholic Fatty Liver Disease in Patients with Type 2 Diabetes Mellitus
Menggege Liu, Qing Zhang, Juan Liu, Huiling Bai, Ping Yang, Xinhua Ye, Xiaoqing Yuan
Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 4169. CrossRef
Sarcopenic Obesity with Normal Body Size May Have Higher Insulin Resistance in Elderly Patients with Type 2 Diabetes Mellitus
Tingting Han, Ting Yuan, Xinyue Liang, Ningxin Chen, Jia Song, Xin Zhao, Yurong Weng, Yaomin Hu
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2022; Volume 15: 1197. CrossRef
Relationship between Visceral Adipose Index, Lipid Accumulation Product and Type 2 Diabetes Mellitus
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S. I. Ibragimova, G. O. Nuskabayeva, Z. N. Shalkharova, K. Zh. Sadykova, G. A. Junusbekova, M. Oran
Diabetes mellitus.2022; 25(3): 226. CrossRef
Changes in body composition and low blood urea nitrogen level related to an increase in the prevalence of fatty liver over 20 years: A cross‐sectional study
Yasushi Imamura, Seiichi Mawatari, Kohei Oda, Kotaro Kumagai, Yasunari Hiramine, Akiko Saishoji, Atsuko Kakihara, Mai Nakahara, Manei Oku, Kaori Hosoyamada, Shuji Kanmura, Akihiro Moriuchi, Hironori Miyahara, Akio ido
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Hye Jin Yoo
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Reduced Skeletal Muscle Volume and Increased Skeletal Muscle Fat Deposition Characterize Diabetes in Individuals after Pancreatitis: A Magnetic Resonance Imaging Study
Andre E. Modesto, Juyeon Ko, Charlotte E. Stuart, Sakina H. Bharmal, Jaelim Cho, Maxim S. Petrov
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Seung Min Chung, Jun Sung Moon, Ji Sung Yoon, Kyu Chang Won, Hyoung Woo Lee
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Association between sarcopenic obesity and higher risk of type 2 diabetes in adults: A systematic review and meta-analysis
Dima Khadra, Leila Itani, Hana Tannir, Dima Kreidieh, Dana El Masri, Marwan El Ghoch
World Journal of Diabetes.2019; 10(5): 311. CrossRef

Obesity and Metabolic Syndrome
Beneficial Effects of Aerobic Exercise Training Combined with Rosiglitazone on Glucose Metabolism in Otsuka Long Evans Tokushima Fatty Rats: Shan-Ji Piao, So Hun Kim, Young Ju Suh, Seong-Bin Hong, Seong Hee Ahn, Da Hae Seo, In-Sun Park, Moonsuk Nam; Diabetes Metab J. 2017;41(6):474-485. Published online November 15, 2017; DOI: https://doi.org/10.4093/dmj.2017.41.6.474

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Background

Regular aerobic exercise is essential for the prevention and management of type 2 diabetes mellitus and may be particularly beneficial for those treated with thiazolidinediones, since it may prevent associated weight gain. This study aimed to evaluate the effect of combined exercise and rosiglitazone treatment on body composition and glucose metabolism in obese diabetes-prone animals.

Methods

We analyzed metabolic parameters, body composition, and islet profiles in Otsuka Long Evans Tokushima Fatty rats after 28 weeks of aerobic exercise, rosiglitazone treatment, and combined exercise and rosiglitazone treatment.

Results

Combined exercise with rosiglitazone showed significantly less increase in weight and epididymal fat compared to rosiglitazone treatment. Aerobic exercise alone and combined rosiglitazone and exercise treatment led to similar retention of lean body mass. All experimental groups showed a decrease in fasting glucose. However, the combined exercise and rosiglitazone therapy group showed prominent improvement in glucose tolerance compared to the other groups. Rescue of islet destruction was observed in all experimental groups, but was most prominent in the combined therapy group.

Conclusion

Regular aerobic exercise combined with rosiglitazone treatment can compensate for the adverse effect of rosiglitazone treatment and has benefit for islet preservation.

Citations

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Shuang Zhang, Yaru Wei, Chunxiao Wang
International Journal of Environmental Research and Public Health.2022; 19(12): 7229. CrossRef
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Habib Yaribeygi, Stephen L. Atkin, Luis E. Simental‐Mendía, Amirhossein Sahebkar
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Obesity and Metabolic Syndrome
Relationship between Regional Body Fat Distribution and Diabetes Mellitus: 2008 to 2010 Korean National Health and Nutrition Examination Surveys: Soo In Choi, Dawn Chung, Jung Soo Lim, Mi Young Lee, Jang Yel Shin, Choon Hee Chung, Ji Hye Huh; Diabetes Metab J. 2017;41(1):51-59. Published online December 21, 2016; DOI: https://doi.org/10.4093/dmj.2017.41.1.51

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Background

The aim of this study was to investigate the association between regional body fat distribution, especially leg fat mass, and the prevalence of diabetes mellitus (DM) in adult populations.

Methods

A total of 3,181 men and 3,827 postmenopausal women aged 50 years or older were analyzed based on Korea National Health and Nutrition Examination Surveys (2008 to 2010). Body compositions including muscle mass and regional fat mass were measured using dual-energy X-ray absorptiometry.

Results

The odds ratios (ORs) for DM was higher with increasing truncal fat mass and arm fat mass, while it was lower with increasing leg fat mass. In a partial correlation analysis adjusted for age, leg fat mass was negatively associated with glycosylated hemoglobin in both sexes and fasting glucose in women. Leg fat mass was positively correlated with appendicular skeletal muscle mass and homeostasis model assessment of β cell. In addition, after adjusting for confounding factors, the OR for DM decreased gradually with increasing leg fat mass quartiles in both genders. When we subdivided the participants into four groups based on the median values of leg fat mass and leg muscle mass, higher leg fat mass significantly lowered the risk of DM even though they have smaller leg muscle mass in both genders (P<0.001).

Conclusion

The relationship between fat mass and the prevalence of DM is different according to regional body fat distribution. Higher leg fat mass was associated with a lower risk of DM in Korean populations. Maintaining leg fat mass may be important in preventing impaired glucose tolerance.

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Obesity and Metabolic Syndrome
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 Metab J. 2016;40(5):386-395. Published online September 27, 2016; DOI: https://doi.org/10.4093/dmj.2016.40.5.386

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Background

Irisin is a myokine implicated in lipid and glucose metabolism. The objective of this study is to examine the effect of a body weight reduction on the serum irisin level and physical indicators in obese Japanese patients without diabetes.

Methods

The subjects were 22 patients (male/female, 5/17; age, 46.1±16.0 years; body mass index [BMI], 36.9±5.0 kg/m²) who completed a 6-month body weight reduction program at our clinic. The program included diet, exercise therapy and cognitive behavioral therapy. Blood parameters, body composition, exercise tolerance, homeostasis model assessment of insulin resistance (HOMA-IR), and serum irisin were determined before and after intervention, and relationships among changes in these data were examined.

Results

There were significant decreases in body weight and BMI after the intervention. Irisin before the intervention was significantly positively correlated with HOMA-IR (r=0.434, P<0.05). The mean irisin level showed no significant change after the intervention in all participants. However, improvements in % body fat, subcutaneous fat area, triglycerides, and fasting glucose were significantly greater in patients with an increase in irisin compared to those with a decrease in irisin after the intervention. Patients with an increase in irisin also had significantly lower fasting insulin (9.7±4.8 vs. 16.4±8.2, P<0.05) and HOMA-IR (2.2±1.1 vs. 3.7±1.6, P<0.05) after the intervention, compared to patients with a decrease in irisin.

Conclusion

Body weight reduction did not alter irisin levels. However, irisin may play important roles in fat and glucose metabolism and insulin resistance, and the effects of body weight reduction on irisin kinetics may be a key for obesity treatment.

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N. M. Sukri, A. Ahmad, N. A. Roos, M. F. Nordin, F. N. Halim, J. V. Gnanou, F. A. Manaf
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Obesity and Metabolic Syndrome
Importance of Lean Muscle Maintenance to Improve Insulin Resistance by Body Weight Reduction in Female Patients with Obesity: Yaeko Fukushima, Satoshi Kurose, Hiromi Shinno, Ha Cao Thu, Nana Takao, Hiromi Tsutsumi, Yutaka Kimura; Diabetes Metab J. 2016;40(2):147-153. Published online March 27, 2016; DOI: https://doi.org/10.4093/dmj.2016.40.2.147

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Background

It has recently been suggested that skeletal muscle has an important role in insulin resistance in obesity, in addition to exercise tolerance and the fat index. The aim of this study was to identify body composition factors that contribute to improvement of insulin resistance in female patients with obesity who reduce body weight.

Methods

We studied 92 female obese patients (age 40.9±10.4 years, body mass index 33.2±4.6 kg/m²) who reduced body weight by ≥5% after an intervention program including diet, exercise therapy, and cognitive behavioral therapy. Before and after the intervention, body composition was evaluated by dual-energy X-ray absorptiometry to examine changes in skeletal muscle mass. Homeostasis model assessment of insulin resistance (HOMA-IR) was measured as an index of insulin resistance. Cardiopulmonary exercise was also performed by all patients.

Results

There were significant improvements in body weight (–10.3%±4.5%), exercise tolerance (anaerobic threshold oxygen uptake 9.1%±18.4%, peak oxygen uptake 11.0%±14.2%), and HOMA-IR (–20.2%±38.3%). Regarding body composition, there were significant decreases in total body fat (–19.3%±9.6%), total fat-free mass (–2.7%±4.3%), and % body fat (–10.1%±7.5%), whereas % skeletal muscle significantly increased (8.9%±7.2%). In stepwise multiple linear regression analysis with change in HOMA-IR as the dependent variable, the change in % skeletal muscle was identified as an independent predictor (β=–0.280, R²=0.068, P<0.01).

Conclusion

Improvement of insulin resistance in female obese patients requires maintenance of skeletal muscle mass.

Citations

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Relative Skeletal Muscle Mass Is Associated with Development of Metabolic Syndrome: Byung Sam Park, Ji Sung Yoon; Diabetes Metab J. 2013;37(6):458-464. Published online December 12, 2013; DOI: https://doi.org/10.4093/dmj.2013.37.6.458

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Background

Visceral adiposity is related to insulin resistance. Skeletal muscle plays a central role in insulin-mediated glucose disposal; however, little is known about the association between muscle mass and metabolic syndrome (MS). This study is to clarify the clinical role of skeletal muscle mass in development of MS.

Methods

A total of 1,042 subjects were enrolled. Subjects with prior MS and chronic diseases were excluded. After 24 months, development of MS was assessed using NCEP-ATP III criteria. Skeletal muscle mass (SMM; kg), body fat mass (BFM; kg), and visceral fat area (VFA; cm²) were obtained from bioelectrical analysis. Then, the following values were calculated as follows: 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), and skeletal muscle to visceral fat ratio (SVR; kg/cm²): SMM (kg)/VFA (cm²).

Results

Among 838 subjects, 88 (10.5%) were newly diagnosed with MS. Development of MS increased according to increasing quintiles of BMI, SMM, VFA, and SMI, but was negatively associated with SMM%, MFR, and SVR. VFA was positively associated with high waist circumference (WC), high blood pressure (BP), dysglycemia, and high triglyceride (TG). In contrast, MFR was negatively associated with high WC, high BP, dysglycemia, and high TG. SVR was negatively associated with all components of MS.

Conclusion

Relative SMM ratio to body composition, rather than absolute mass, may play a critical role in development of MS and could be used as a strong predictor.

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