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Abstract

BACKGROUND:

In adults with type 2 diabetes (T2DM), sarcopenia and obesity are two common body composition issues.

OBJECTIVE:

We investigated the associated influencing factors of muscle mass loss in obese adults with T2DM, to provide a theoretical basis for the prevention of sarcopenic obesity in patients with T2DM.

METHODS:

We recruited 315 participants in this study. The participants underwent body composition assessment and clinical information was collected. Dual-energy X-ray absorptiometry was used to verify the accuracy of the body composition data. Based on their body fat percentage, 189 patients with T2DM were classified as obese. Patients with T2DM and obesity were grouped into the muscle mass loss group and non-muscle mass loss group based on gender. We collected demographic and clinical information about patients with T2DM who were obese, including their age, gender, body mass index (BMI), appendicular skeletal muscle index (ASMI), and body fat percentage (PBF).

RESULTS:

Among the participants who were obese and had T2DM, 56.61% (107/189) experienced muscle mass loss, with a detection rate of 43.42% (33/76) among females and 65.49% (74/113) among males. Body mass index, fat index, Android fat, Gynoid fat, limb fat, trunk fat, and total body bone mineral content were all lower in the muscle mass loss group compared to the non-muscle mass loss group, regardless of gender (all $P<$ 0.001). Muscle mass loss in obese adults with T2DM was affected by BMI, body fat index, and limb fat.

CONCLUSION:

Muscle mass loss is more prevalent in adults with T2DM and a high PBF. Body mass index, body fat index, and limb fat are the protective factors of muscle mass loss in adult patients with T2DM and obesity.

Keywords

Appendicular skeletal muscle index (ASMI)influencing factors muscle mass loss obesity Type 2 diabetes mellitus (T2DM)

1. Introduction

In adults with type 2 diabetes (T2DM), sarcopenia and obesity are two common body composition issues. Sarcopenia refers to a condition characterized by a significant decrease in muscle mass, strength, and/or physical functioning. Studies indicate that low muscle mass not only aggravates metabolic abnormalities [1], but is also a strong predictor of increased disability, disability rate, and mortality [2]. Adults with T2DM experience a skeletal muscle mass loss in the early stages of the disease, and diabetes can aggravate age-related loss of muscle mass [3, 4, 5].

Obesity is a chronic nutritional metabolic disease caused by abnormal accumulation and (or) abnormal distribution of fat in the body due to an energy imbalance. Recent studies have linked obesity to a loss of muscle mass, an effect that compounds the metabolic disorder already existing in people with diabetes. Loss of muscle mass and weight gain both have negative effects on metabolism in people with diabetes [6, 7]. Patients with T2DM are more likely to be obese or overweight, but little is known about the detection rate and associated influencing factors of muscle mass loss in patients with both T2DM and obesity. The purpose of this study was to examine the prevalence of muscle mass loss in patients with T2DM and obesity and the factors influencing obesity with respect to skeletal muscle content in an effort to provide a theoretical basis for preventing sarcopenic obesity in patients with T2DM. Participants included adult patients with T2DM and obesity hospitalized in the endocrinology department.

2. Materials and methods

2.1 Participants

This was a cross-sectional study of adults with T2DM. Between January and December 2021, we enrolled 315 adult patients with T2DM who were admitted to the Endocrinology Department of the China Rehabilitation Research Center. The patients had a mean age of 62.18 $\pm$ 11.71 years and a mean body mass index (BMI) of 25.96 $\pm$ 3.92 kg/m ${}^{2}$ . World Health Organization (WHO) diagnostic criteria for diabetes from 1999 were used to establish the criteria and classification for T2DM [8]. All participants signed the informed consent form.

Inclusion criteria: (1) Age $\geqslant$ 18 years; (2) underwent dual-energy X-ray absorptiometry (DXA) examination. Exclusion criteria: (1) Any endocrine system disease (e.g., hyperthyroidism, Cushing’s syndrome), severe systemic disease, neuromuscular disease, and severe malnutrition requiring treatment; (2) History of infectious diseases or malignant tumors, patients under long-term bed rest, and tuberculosis; (3) Long-term use of oral or intravenous corticosteroids, thyroid hormone, growth hormone, weight loss drugs, and other drugs; (4) Underwent bariatric surgery previously; (5) With metal stent or pacemaker in the body, which can affect the accuracy of body composition analysis. This study was reviewed and approved by the Medical Ethics Committee of the China Rehabilitation Research Center (Approval no. 2020-059-1).

2.2 Collection of clinical data

The age, gender, height, weight, BMI, blood pres-sure, duration of diabetes, past medical history, and drug combination of all participants were recorded. After an overnight fast of 8 hours, venous blood was drawn from each participant’s elbow to measure a variety of biochemical markers. These included glycosylated hemoglobin, fasting blood glucose, aspartate transaminase, alanine transaminase, urea, creatinine, total cholesterol, and triglyceride, among others.

2.3 Body composition evaluation

Body composition data were measured using Discovery Wi Dual-energy X-ray absorptiometry manufactured by Hologic (USA), including the total body and limb skeletal muscle mass, fat mass index (FMI), Android fat, gynoid fat, Android fat/gynoid fat percentage (A/G), percentage of body fat (PBF), limb and trunk fat mass, and total body bone mineral content (BMC). All the scans were performed by a single experienced technologist.

2.4 Diagnostic criteria and grouping

Obesity was primarily diagnosed based on the PBF. When the PBF exceeded 60% of the population of the same age (that is, PBF $\geqslant$ 40% for females and PBF $\geqslant$ 28% for males), the patient was diagnosed with obesity [9]; the skeletal muscle mass was evaluated using the appendicular skeletal muscle index (ASMI), ASMI $=$ skeletal muscle mass of limbs/height ${}^{2}$ (kg/m ${}^{2})$ . According to the criteria of the Asian Working Group for Sarcopenia (AWGS), muscle mass loss was diagnosed when ASMI $<$ 7.0 kg/m ${}^{2}$ for males and $<$ 5.4 kg/m ${}^{2}$ for females [10]. We recruited 315 participants in this study who fulfilled the inclusion and exclusion criteria. The participants underwent body composition assessment and reliable clinical information was collected. Based on their PBF, 189 patients with T2DM were classified as obese. Muscle mass loss was found in 175 of the 315 patients with T2DM, with a detection rate of 55.56%. Patients with T2DM and obesity who have lost muscle mass were separated from those who have not lost muscle mass by gender into two groups – muscle mass loss group and non-muscle mass loss group, respectively.

2.5 Statistical analysis

Data were processed and analyzed using SPSS 24.0 statistical software. The measurement data were subjected to a normal test, and the data in normal distribution are expressed as mean $\pm$ standard deviation ( $\bar{x}\pm s$ ); the independent-samples $t$ -test was utilized to compare the two groups. The Pearson’s correlation test was used to determine the relationship between variables. We used multiple linear regression to analyze the influencing factors of ASMI. All the tests were two-sided, and the difference was statistically significant at $P<$ 0.05.

3. Results

3.1 Screening process and results

Obesity was diagnosed in 189 of the 315 patients with T2DM included in the study, including 113 males (average age 61.42 $\pm$ 13.51 years) and 76 females (average age 65.12 $\pm$ 9.32 years). The detection rate of muscle mass loss in patients with T2DM and obesity was 56.61% (107/189), with 43.42% (33/76) for females and 65.49% (74/113) for males.

3.2 Comparison of clinical data

Males in the muscle mass loss group were significantly older than those in the non-muscle mass loss group ( $P<$ 0.001). Regardless of gender, the muscle mass loss group had lower BMI, ASMI, arm skeletal muscle mass (SMM), leg SMM, trunk SMM, total SMM, and BMC than the non-muscle mass loss group (all $P<$ 0.05, Table 1).

Table 1
A comparison of gender-specific clinical data

Item	Male			Female
	Non-muscle mass loss group ( $n=$ 39)	Muscle mass loss group ( $n=$ 74)	P value	Non-muscle mass loss group ( $n=$ 43)	Muscle mass loss group ( $n=$ 33)	P value
Age (years)	53.10 $\pm$ 13.16	65.80 $\pm$ 11.56	$<$ 0.001	64.14 $\pm$ 9.80	66.39 $\pm$ 8.62	0.299
Course of disease (years)	12.67 $\pm$ 7.42	13.58 $\pm$ 8.76	0.580	12.87 $\pm$ 9.41	14.15 $\pm$ 10.26	0.625
BMI (kg/m ${}^{2})$	31.21 $\pm$ 3.27	25.29 $\pm$ 2.86	$<$ 0.001	29.41 $\pm$ 2.93	25.21 $\pm$ 2.39	$<$ 0.001
HbA ${}_{1c}$ (%)	8.94 $\pm$ 1.80	9.27 $\pm$ 1.93	0.377	8.84 $\pm$ 1.88	8.91 $\pm$ 1.68	0.850
FPG (mmol/L)	8.20 $\pm$ 2.61	8.59 $\pm$ 2.78	0.458	8.21 $\pm$ 2.97	8.47 $\pm$ 2.49	0.620
TC (mmol/L)	5.30 $\pm$ 1.01	5.19 $\pm$ 1.23	0.595	5.63 $\pm$ 1.22	5.26 $\pm$ 1.15	0.788
TG (mmol/L)	2.26 $\pm$ 0.88	2.12 $\pm$ 1.11	0.455	2.75 $\pm$ 0.84	2.54 $\pm$ 0.69	0.072
ASMI (kg/m ${}^{2})$	8.24 $\pm$ 0.83	6.40 $\pm$ 0.62	$<$ 0.001	6.13 $\pm$ 0.55	5.02 $\pm$ 0.35	$<$ 0.001
Arm SMM (kg)	6.96 $\pm$ 1.44	4.88 $\pm$ 0.74	$<$ 0.001	3.85 $\pm$ 0.66	2.96 $\pm$ 0.50	$<$ 0.001
Leg SMM (kg)	17.35 $\pm$ 2.47	13.81 $\pm$ 1.63	$<$ 0.001	11.63 $\pm$ 1.32	9.56 $\pm$ 1.15	$<$ 0.001
Trunk SMM (kg)	29.22 $\pm$ 4.02	24.11 $\pm$ 2.80	$<$ 0.001	20.98 $\pm$ 2.32	18.02 $\pm$ 2.24	$<$ 0.001
Total SMM (kg)	57.60 $\pm$ 7.22	46.48 $\pm$ 4.89	$<$ 0.001	39.72 $\pm$ 4.01	33.59 $\pm$ 3.65	$<$ 0.001
BMC (kg)	2.52 $\pm$ 0.31	2.19 $\pm$ 0.32	$<$ 0.001	1.76 $\pm$ 0.33	1.60 $\pm$ 0.30	0.036

Values are mean $\pm$ SD. BMI: body mass index; HbA1c: hemoglobin A1c; FPG: fasting plasma glucose; TC: total cholesterol; TG: triglyceride; ASMI: appendicular skeletal muscle index; SMM: skeletal muscle mass; BMC: bone mineral content.

Table 2

Comparison of body fat between non-muscle mass loss and muscle mass loss groups

Item	Male			Female
	Non-muscle mass loss group ( $n=$ 39)	Muscle mass loss group ( $n=$ 74)	P value	Non-muscle mass loss group ( $n=$ 43)	Muscle mass loss group ( $n=$ 33)	P value
FMI (kg/m ${}^{2})$	10.42 $\pm$ 2.07	8.21 $\pm$ 1.67	$<$ 0.001	12.90 $\pm$ 2.04	10.95 $\pm$ 1.54	$<$ 0.001
Android FAT (kg)	3.34 $\pm$ 0.82	2.46 $\pm$ 0.61	$<$ 0.001	3.05 $\pm$ 0.61	2.43 $\pm$ 0.45	$<$ 0.001
Gynoid FAT (kg)	2.04 $\pm$ 0.62	1.48 $\pm$ 0.41	$<$ 0.001	2.31 $\pm$ 0.57	1.91 $\pm$ 0.43	$<$ 0.001
A/G	1.52 $\pm$ 0.25	1.43 $\pm$ 0.25	0.074	1.17 $\pm$ 0.17	1.08 $\pm$ 0.11	0.013
Limbs FAT (kg)	12.24 $\pm$ 3.51	8.97 $\pm$ 2.02	$<$ 0.001	13.63 $\pm$ 2.93	11.17 $\pm$ 1.96	$<$ 0.001
Trunk FAT (kg)	17.09 $\pm$ 3.85	13.63 $\pm$ 2.95	$<$ 0.001	17.67 $\pm$ 2.94	14.97 $\pm$ 2.59	$<$ 0.001
PBF (%)	33.57 $\pm$ 3.46	32.74 $\pm$ 3.39	0.220	43.77 $\pm$ 3.03	44.02 $\pm$ 2.64	0.706
Limbs PBF (%)	31.85 $\pm$ 4.32	30.99 $\pm$ 4.23	0.311	45.14 $\pm$ 4.24	46.05 $\pm$ 4.07	0.349
Trunk PBF (%)	36.10 $\pm$ 3.62	35.29 $\pm$ 3.75	0.274	45.04 $\pm$ 3.33	45.14 $\pm$ 3.30	0.901

Values are mean $\pm$ SD. FMI: fat mass index; A/G: Android fat/gynoid fat percentage; PBF: percentage of body fat.

3.3 Comparison of total and local body fat between different groups

FMI, Android fat, gynoid fat, limb fat, and trunk fat in the muscle mass loss group were lower than those in the non-muscle mass loss group (all $P<$ 0.001); the A/G of females in the muscle mass loss group was lower than that in the non-muscle mass loss group ( $P<$ 0.05); there was no statistically significant difference between the muscle mass loss group and the non-muscle mass loss group in terms of the total PBF and local PBF (both $P>$ 0.05, Table 2).

3.4 Correlation between ASMI and various influencing factors in patients with T2DM and obesity

Pearson’s correlation analysis showed that the ASMI in males was positively correlated with BMI, FMI, Android fat, gynoid fat, limb fat, and BMC ( $r=$ 0.838, 0.613, 0.630, 0.509, 0.642, 0.562, $P<$ 0.001), and was negatively correlated with age ( $r=$ $-$ 0.532, $P<$ 0.001). ASMI in females was positively correlated with BMI, FMI, Android fat, gynoid fat, limb fat, BMC, and A/G ( $r=$ 0.815, 0.603, 0.606, 0.462, 0.621, 0.457, 0.242, $P<$ 0.05), and was negatively correlated with age ( $r=$ $-$ 0.287, $P<$ 0.05). No significant correlation was found between ASMI and PBF and limb PBF and trunk PBF ( $P>$ 0.05).

3.5 Multiple linear regression analysis of ASMI in patients with T2DM and obesity of different genders

Using ASMI as the dependent variable and age, BMI, FMI, Android fat, gynoid fat, limb fat, and BMC as the independent variables, we developed gender-specific multiple linear regression analysis models. The results indicated that BMI, FMI, and limb fat were the influencing factors of ASMI in patients with T2DM and obesity ( $P<$ 0.001, Table 3).

4. Discussion

Low muscle mass is an essential indicator for diagnosing sarcopenia and is also a strong predictor of increased body disability, disability rate, and mortality [2]. Kim et al. [11] examined elderly patients with T2DM with BMI $<$ 24 kg/m ${}^{2}$ and found that the detection rate of muscle mass loss was 57.6%. The study by Yuan et al. [12] revealed that the prevalence rate of muscle mass loss in patients with T2DM was 42.01%. Muscle mass loss is common among patients with T2DM, but the detection rate of muscle mass loss in patients with T2DM and obesity is rarely reported. In this study, we utilized the body composition detection method – DXA method, which is the most widely recommended method in the world. For muscle mass loss, the AWGS-recommended diagnostic criteria applicable to the Asian population were used. We selected 315 adult patients with T2DM (BMI 25.96 $\pm$ 3.92 kg/m ${}^{2})$ hospitalized in our department; 55.56% of them had low muscle mass, which was similar to the results of a previous study [11]; the PBF threshold was used to diagnose obesity. A total of 189 patients with T2DM and obesity were included. The results revealed that 56.61% of patients with T2DM and obesity had low muscle mass with a detection rate of 65.49% among males and 43.42% among females, indicating that a substantial number of patients with T2DM and obesity with high PBF had low muscle mass. Therefore, the purpose of this study was to provide a theoretical basis for preventing patients with T2DM and obesity with low muscle mass from progressing to sarcopenic obesity by analyzing the related influencing factors for these populations.

4.1 Effect of BMI on muscle mass loss in patients with T2DM and obesity

Previous studies have confirmed that patients with T2DM with a low BMI have an increased risk of muscle mass loss and sarcopenia [12, 13, 14, 15, 16]. The results of our study revealed that the BMI of patients with T2DM and obesity in the muscle mass loss group was lower than that of the non-muscle mass loss group, irrespective of gender. Consistent with the findings of a previous study [12], BMI is a protective factor for muscle mass loss. There is a strong correlation between BMI and ASMI in this study, suggesting that BMI can be used as an indicator of muscle mass in patients with T2DM and obesity with a high PBF. The weight criteria based on BMI in China are 24.0–27.9 for overweight, and $\geqslant$ 28.0 for obesity. Most patients with diabetes and obesity with low muscle mass had a BMI in the overweight range, according to the results of our study. If the body composition characteristics are ignored, patients who do not actively reduce fat and increase muscle may develop sarcopenic obesity, which has detrimental effects on the body.

Table 3
A multiple linear regression analysis of ASMI in obese adults with T2DM

Gender	Factor	Unstandardized coefficient $\beta$	SE	Standardized coefficient $\beta$	$t$ value	$P$ value
Male	BMI	0.458	0.022	1.685	20.712	$<$ 0.001
	FMI	$-$ 0.671	0.052	$-$ 1.254	$-$ 12.834	$<$ 0.001
	Limbs FAT	0.000	0.000	0.372	5.554	$<$ 0.001
Female	BMI	0.296	0.025	1.378	11.817	$<$ 0.001
	FMI	$-$ 0.366	0.044	$-$ 1.035	$-$ 8.234	$<$ 0.001
	Limbs FAT	0.000	0.000	0.473	5.418	$<$ 0.001

BMI: body mass index; FMI: fat mass index; ASMI: appendicular skeletal muscle index.

4.2 Effect of FMI and limb fat percentage on muscle mass loss in patients with T2DM and obesity

Upper and lower limb fat content are indicative of subcutaneous fat accumulation, while FMI adjusts for the impact of height on body fat content to provide an accurate reflection and representation of obesity as a whole. Although the PBF was comparable between the two groups, the FMI and the fat content of upper and lower limbs were significantly lower in the muscle mass loss group than in the non-muscle mass loss group. ASMI was positively correlated with FMI and the fat content of the upper and lower limbs in this study. FMI and the fat content of the upper and lower limbs are protective factors of muscle mass loss, which is consistent with the conclusion of a previous study conducted by Cheng et al. [17], namely that adipose tissues protect muscles. Moreover, Perna et al. [18] concluded that subcutaneous adipose tissue may have a protective effect on poor prognosis. The results of this study indicated that the peripheral fat content of limbs was closely related to the decrease in skeletal muscle content in adult patients with T2DM and obesity, highlighting the importance of protecting limb fat during weight loss.

Currently, PBF is the most widely used criterion for assessing obesity in the sarcopenic obese population. We observed that total and local PBFs were comparable between the muscle mass loss group and the non-muscle mass loss group, irrespective of gender. In the muscle mass loss group, not only did the FMI and limb fat content decrease, but the android fat reflecting the central obesity decreased as well; ASMI was not correlated with total and local PBFs but was positively correlated with the total and local fat content. Previous studies concluded that an increase in adipose tissue, especially visceral fat, facilitated muscle mass loss [19, 20]. Contrary to previous findings, the results of our study revealed that patients with diabetes and obesity with muscle mass loss had lower and total fat content than those without muscle mass loss. Adipose tissue is closely associated with muscle, and the coexistence of obesity and sarcopenia is not a simple combination, but rather the result of the interaction of multiple factors including ageing, lifestyle (nutritional and exercise status), insulin resistance, chronic inflammation, and hormones (corticosteroids, growth hormone, insulin-like growth factor, muscle somatostatin, and sex hormone) [21, 22]. The precise mechanism of the interaction between muscle and adipose tissue must be further clarified by additional research.

Patients with diabetes are at increased risk for the development of sarcopenic obesity when they have high PBF but low muscle mass. Such patients can mitigate the negative effects of sarcopenic obesity through proper nutrition and sports rehabilitation management. Other rehabilitation interventions have also been shown to improve muscle properties, according to a few studies [23, 24]. Our findings suggest that future efforts to prevent sarcopenic obesity should prioritize early screening and identification of such patients, as well as the active implementation of rehabilitation strategies to reduce fat and increase muscle mass.

4.3 Limitations

One of the limitations of the present study is the small sample size. In addition, our sample was only selected from the China Rehabilitation Research Center, so the results cannot be generalizable to the broader T2DM population. Important clinical implications for understanding the mechanism of action between skeletal muscle content and adipose tissue in patients with diabetes can be gleaned from measuring abdominal subcutaneous fat, visceral fat, and intramuscular fat deposition using computed tomography or magnetic resonance imaging scans in the follow-up study.

5. Conclusion

Patients with T2DM and obesity who have a high PBF, have a disproportionately low percentage of muscle mass. Protective factors of ASMI in adult patients with T2DM and obesity include BMI, FMI, and limb fat content. As the clinical manifestations of patients with T2DM with fat accumulation and muscle mass loss are hidden, early screening and identification of such patients, as well as formulating an intervention strategy of actively reducing fat and increasing muscle mass through a combination of caloric restriction with nutrition and exercise training may be the most important tasks in the future for preventing sarcopenic obesity.

Ethics statement

The study was approved by the Ethics Committee of China Rehabilitation Research Center (Approval no. 2020-059-1) and conducted in accordance with the Declaration of Helsinki.

Funding

The study was funded by the Scientific Research Fund Project of China Rehabilitation Research Center (No. 2020-09).

Informed consent

Written informed consent was obtained from all participants.

Author contributions

LNB and XZ conceived the idea and conceptualised the study. LNB, YYQ, SH, CL and YZ collected the data. LNB, XZ, YYQ, SH, CL and YZ analysed the data. LNB obtained the financing. LNB drafted the manuscript, then LNB, XZ, YYQ, SH, CL and YZ reviewed the manuscript. All authors read and approved the final draft.

Footnotes

Acknowledgments

The authors would like to acknowledge the hard and dedicated work of all staff that implemented the intervention and evaluation components of the study.

Conflict of interest

The authors declare that they have no competing interests.

References

Park

Goodpaster

Lee

, et al. Excessive loss of skeletal muscle mass in older adults with type 2 diabetes. Diabetes Care. 2009; 32(11): 1993-1997. doi: 10.2337/dc09-0264.

Auyeung

Lee

Leung

, et al. These lection of a screening test for frailty identification incommunity dwelling older adults. J Nutr Health Aging. 2014; 18(2): 199-203. doi: 10.1007/s12603-12013-0365-4.

Leenders

Verdijk

van der Hoeven

, et al. Patients with type 2 diabetes show a greater decline in muscle mass, muscle strength, and functional capacity with aging. J Am Med Dir Assoc. 2013; 14(8): 585-592. doi: 10.1016/j.jamda.2013.02.006.

Kim

Park

Yang

, et al. Prevalence and determinant factors of sarcopenia in patients with type 2 diabetes: the Korean Sarcopenic Obesity Study (KSOS)Diabetes. Care. 2010; 33(7): 1497-1499.

Guerrero

Bunout

Hirsch

, et al. Premature loss of muscle mass and function in type 2 diabetes. Diabetes Res Clin Pract. 2016; 117: 32-38.

Park

Goodpaster

Lee

, et al. Excessive loss of skeletal muscle mass in older adults with type 2 diabetes. Diabetes Care. 2009; 32(11): 1993-1997. doi: 10.2337/dc09-0264.

Chen

Woo

Assantachai

, et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020; 21(3): 300-307.e2. doi: 10.1016/j.jamda.2019.12.012.

World Health Organization. Definition,diagnosis and classification of diabetes mellitus and its complications: report of a WHO consultation. Part 1, Diagnosis and classification of diabetes mellitus. Geneva World Health Organization, 1999.

Baumgartner

Wayne

Waters

, et al. Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Obes Res. 2004; 12(12): 1995-2004.

10.

Chen

Liu

Woo

, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc. 2014; 15(2): 95-101.

11.

Kim

Park

Kim

, et al. Type 2 diabetes is associated with low muscle mass in older adults. Geriatr Gerontol Int. 2014; 14(Suppl 1): 115-121.

12.

Duan

Zhang

Kong

, et al. Multivariate analysis of muscle mass loss in patients with type 2 diabetes. Chinese Journal of Endocrinology and Metabolism. 2020; 36(9): 778-782. doi: 10.3760/cma.j.cn311282-20191123-00512.

13.

Kim

Suzuki

Kim

, et al. J Am Med Dir Assoc. 2015; 16(1): 85.e1-e8. doi: 10.1016/j.jamda.2014.10.006.

14.

Fukuoka

Narita

Fujita

, et al. Importance of physical evaluation using skeletal muscle mass index and body fat percentage to prevent sarcopenia in elderly Japanese diabetes patients. J Diabetes Investig. 2019; 10(2): 322-330.

15.

Graf

Pichard

Herrmann

, et al. Prevalence of low muscle mass according to body mass index in older adults. Nutrition. 2017; 34: 124-129.

16.

Kim

Park

Yang

, et al. Prevalence and determinant factors of sarcopenia in patients with type 2 diabetes: the Korean Sarcopenic Obesity Study(KSOS). Diabetes Care. 2010; 33(7): 1497-1499.

17.

Cheng

Zhu

Zhang

, et al. A cross-sectional study of loss of muscle mass corresponding to sarcopenia in healthy Chinese men and women: reference values,prevalence,and association with bone mass. J Bone Miner Metab. 2014; 32: 78-88.

18.

Perna

Spadaccini

Rondanelli

. Sarcopenic obesity: time to target the phenotypes. J Cachexia Sarcopenia Muscle. 2019; 10(3): 710-711.

19.

Ilich

Kelly

Inglis

, et al. Interrelationship among muscle, fat, and bone: Connecting the dots on cellular, hormonal, and whole body levelsAgeing. Res Rev. 2014; 15: 51-60. doi: 10.1016/j.arr.2014.02.007.

20.

Hita-Contreras

Martinez-Amat

Cruz-Diaz

, et al. Osteosarcopenic obesity and fall prevention strategies. Maturitas. 2014; 11: 009.

21.

Shao

Campbell

Chen

CYO

, et al. The emerging global phenomenon of sarcopenic obesity: role of functional foods; a conference report. J Funct Foods. 2017; 33: 244-250. doi: 10.1016/j.jff.2017.03.048.

22.

Scott

Cumming

Naganathan

, et al. Associations of sarcopenic obesity with the metabolic syndrome and insulin resistance over five years in older men: The Concord Health and Ageing in Men Project. Exp Gerontol. 2018; 108: 99-105. doi: 10.1016/j.exger.2018.04.006.

23.

Marisa

Riccardo

Giacomo

Giulia

Franca

Pietro

Giancarlo

. Pain and muscles properties modifications after botulinum toxin type a (BTX-A) and radial extracorporeal shock wave (rESWT) combined treatment. Endocr Metab Immune Disord Drug Targets [Internet]. 2019; 19(8): 1127-33.

24.

Farì

Santagati

Macchiarola

Ricci

Di Paolo

Caforio

Invernizzi

Notarnicola

Megna

Ranieri

. Musculoskeletal pain related to surfing practice: Which role for sports rehabilitation strategies? A cross-sectional study. J Back Musculoskelet Rehabil [Internet]. 2022; 35(4): 911-7.

Prevalence and influencing factors of muscle mass loss in adults with diabetes and a high body fat percentage: A cross-sectional study

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

2.1 Participants

2.2 Collection of clinical data

2.3 Body composition evaluation

2.4 Diagnostic criteria and grouping

2.5 Statistical analysis

3. Results

3.1 Screening process and results

3.2 Comparison of clinical data

Table 1 A comparison of gender-specific clinical data

3.4 Correlation between ASMI and various influencing factors in patients with T2DM and obesity

3.5 Multiple linear regression analysis of ASMI in patients with T2DM and obesity of different genders

4. Discussion

4.1 Effect of BMI on muscle mass loss in patients with T2DM and obesity

Table 3 A multiple linear regression analysis of ASMI in obese adults with T2DM

4.3 Limitations

5. Conclusion

Ethics statement

Funding

Informed consent

Author contributions

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
A comparison of gender-specific clinical data

Table 3
A multiple linear regression analysis of ASMI in obese adults with T2DM