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Skeletal Muscle Mass Ratio as an Index for Sarcopenia in Patients With Type 2 Diabetes

Shimizu, Ryo MS; Tando, Yusuke MD, PhD; Yokoyama, Asami MS; Yanagimachi, Miyuki MD, PhD

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doi: 10.1097/TIN.0000000000000179
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THE TREATMENT aims for type 2 diabetes mellitus (T2DM) include maintaining good glycemic control and preventing cardiovascular disease, thereby contributing to a good quality of life. Given that the treatment of this hyperglycemic condition requires improvement in insulin resistance, which is induced by obesity, dietary therapy is essential for achieving a desirable weight.

Older adults show declines in various physical functions along with aging. Declines in muscle mass, muscle strength, and physical function with aging are referred to as sarcopenia.1 Multiple criteria for sarcopenia diagnosis have been proposed; however, the criteria recommended by the Asian Working Group for Sarcopenia2 are commonly used in Japan. The parameters used to diagnose sarcopenia are the patient's age, appendicular skeletal muscle mass index (ASMI: limb skeletal muscle mass [kg]/height2 [m2]), and grip strength or gait velocity.

Multiple reports indicate that T2DM is associated with increased risks of sarcopenia.3–5 For example, a study of 1090 Chinese individuals aged 60 years and older in 2016 confirmed that the prevalence of sarcopenia was significantly higher among T2DM patients than among healthy controls (14.8% vs 11.2%). Furthermore, the incidence of presarcopenia with low ASMI was 14.4% among T2DM patients and 8.4% among healthy controls.5 The mechanism underlying the occurrence of sarcopenia in T2DM is related to muscle weakness due to increased severity of neuropathy,6 decreased muscle fiber density, incomplete reinnervation after axonal loss,7 and worsening of insulin resistance. Insulin has an anabolic action; it facilitates muscle protein synthesis.8 Furthermore, variations in muscle protein synthesis, resulting from insulin stimulation, are negatively correlated with the homeostasis model assessment as an index of insulin resistance (HOMA-IR). This decrease in insulin sensitivity indicates the involvement of skeletal muscle protein metabolism in T2DM patients.9 In T2DM patients with obesity, proinflammatory cytokines produced because of the invasion of adipocytes by macrophages are considered to increase insulin resistance and muscle weakness.10

To maintain and increase the amount of muscle, it is vital that one consume protein and other nutrients in sufficient amounts. Among the nutritional therapies practiced in Japan, there are quite a few that limit energy intake to correct for obesity and maintain recommended body weight. Consequently, it is highly likely that, especially in the elderly, sarcopenia is linked to protein and other nutrient deficiencies caused by limited calories.

From these facts, we believe that it is important to make note of the possibility of sarcopenia in T2DM patients, and even when attempting to treat obesity, implement nutrition and exercise therapies that take into account not just body weight but body composition—in particular, muscle. This focus will improve obesity while keeping sarcopenia in perspective and treating T2DM patients. We previously reported that ASMI, an index of sarcopenia, and the parameters for evaluating muscle mass were insufficient in evaluating muscle mass in T2DM patients whose disease background frequently includes obesity.11 Currently, no criteria are commonly recognized to determine sarcopenic obesity in patients with both obesity and sarcopenia. However, a few studies have demonstrated the use of skeletal muscle mass ratio (SMR: skeletal muscle mass [kg]/weight [kg]) when the body composition is measured using bioelectrical impedance analysis.12 Furthermore, given that the recommended criteria for sarcopenia diagnosis include ASMI, which focuses on limb skeletal mass, it might be useful to evaluate muscle mass on the basis of appendicular skeletal muscle mass ratio (ASMR: limb skeletal muscle mass [kg]/weight [kg]).

The relationship between 1-year variations in SMR, ASMI, and ASMR and those in glycated hemoglobin (HbA1c) was analyzed in this study because an increase or decrease in muscle mass may affect glycemic control. Second, this study examined the correlation among SMR, ASMI, ASMR (indices for the evaluation of muscle mass), and grip strength values in T2DM patients to determine an appropriate index of sarcopenia. Finally, the relationship between nutrients involved in muscle synthesis and the indices was also analyzed to assess nutrients that should be considered during the treatment of T2DM and sarcopenia.


The T2DM patients who visited the Department of Endocrinology and Metabolism, Hirosaki University Hospital, between August and October 2015 were asked to participate in the study. Consent was obtained from 12 men and 14 women who were included as participants in this study. The inclusion criterion for this study was that the participants be older than 40 years. Subjects were excluded if they were unable to respond on their own to the self-administered questionnaire, given as part of the research cooperation request. It was considered useful to examine age-induced changes in muscle amount, and therefore we targeted a wide age range of middle-aged adults and older.

Laboratory test results of the participants' early morning fasting blood samples, collected during their physical examinations, were extracted from the medical records. On the same day as blood collection, patients underwent physical and body composition measurements and, several days later, received a dietary survey. One year later, between August and October 2016, their medical records were reviewed again to collect the results of the biochemical tests that subjects underwent for clinical evaluation. On the day of evaluation, each subject's height, body composition, and grip strength were measured. Other information, including participants' age and their medications, was obtained from the medical records.

The participants' heights were measured using a stadiometer. Body weight, fat mass, and muscle mass were measured using the InBody 770 Body Composition Analyzer (InBody Japan Inc, Tokyo, Japan), a bioelectrical impedance measuring instrument. Body mass index (BMI) was calculated by dividing the body weight (kg) by the square of the height (m2). Skeletal muscle mass ratio was calculated by dividing the skeletal muscle mass by body weight, and ASMR was calculated by dividing the total mass of the 4 limbs by body weight. Appendicular skeletal muscle mass index, the index of sarcopenia, was calculated by dividing the total mass of the 4 limbs by the square of the height. Grip strength was measured using the TL110 T-2168 (Toei Light Co., Ltd, Saitama, Japan), a digital grip strength tester.

Data from a self-administered, brief diet history questionnaire (BDHQ) were collected. Energy and nutrient intakes were confirmed and estimated. The BDHQ results were reported to be relatively reasonable compared with the use of 16-day semiweighed dietary records.13 Upon request, the BDHQ support center analyzed the answers, examining the intake amounts of nutrients and other dietary items.

All statistical analyses were conducted using SPSS Statistics version 22 (IBM Corp, Armonk, New York). A corresponding Student t test or Wilcoxon signed rank test comparing the 2 groups was used. For the correlation analysis, Pearson product-moment correlation coefficient or Spearman rank correlation coefficient was used. In addition, a stepwise regression analysis was performed with the use of factors having a single significant correlation as the independent variables and HbA1c level and SMR as dependent variables. The P values were considered statistically significant at less than .05. Data are reported as mean ± standard deviation, median (interquartile range), or number (proportion).

The study protocol was approved by the Ethics Review Committee of Hirosaki University Hospital (no. 2015-022). The study was conducted in accordance with the tenets of the Declaration of Helsinki (as revised in Brazil 2013). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was applied for quality assessment in this study.14


The mean age of the 26 participants was 67.2 ± 9.6 years. The youngest and oldest participants were aged 45 and 84 years, respectively. Among all of the participants, 25 were prescribed oral hypoglycemic medications and glucagon-like peptide-1 injections. Sixteen (64.0%) were undergoing insulin therapy, and 7 (28.0%) were concurrently receiving medications other than insulin.

No significant differences were observed in the physical parameters measured in the 2 survey years (Table 1). The reference range of BMI is 18.5 kg/m2 or more and less than 25 kg/m2 in Japan. A BMI of more than 25 kg/m2 indicates obesity and less than 18.5 kg/m2 indicates low body weight. The median BMI was 25.9 kg/m2 in 2015; no participant had a BMI of less than 18.5 kg/m2. Three men (25.0%) and 10 women (71.4%) had a BMI of 25 kg/m2 or more. Three subjects had an ASMI less than 7.0 kg/m2, 1 of the Asian Working Group for Sarcopenia criteria for sarcopenia in men. All of these subjects were older than 70 years and had normal BMIs. No female subjects had an ASMI lower than the female criterion level, 5.7 kg/m2. In 2016, 1 male subject who previously had an ASMI value lower than the criterion value achieved a normal value, whereas the ASMIs of 2 female subjects, 1 previously normal and 1 previously obese, fell below the criterion value.

Table 1. - Comparison of Biochemical Parameters, Physical Measurements, and Body Composition Measurements Between 2015 and 2016a
2015 2016
n n P
Weight, kg 26 65.8 (61.3-79.8) 26 65.1 (60.6-79.7) .334
BMI, kg/m2 26 25.9 (23.8-30.0) 26 25.0 (24.1-30.6) .411
Body composition
 Body fat mass, kg 26 25.0 ± 12.8 26 25.2 ± 12.5 .686
 Body fat ratio, % 26 34.5 ± 11.6 26 34.8 ± 11.3 .408
 Lean body mass, kg 26 42.2 ± 6.0 26 42.0 ± 6.5 .530
 SMM, kg 26 24.1 ± 3.8 26 24.1 ± 4.1 .694
 SMR, % 26 35.5 (29.8-41.0) 26 34.0 (30.1-40.6) .416
 ASMM, kg 26 18.2 ± 3.1 26 18.3 ± 3.4 .596
 ASMR, % 26 26.8 ± 5.0 26 26.7 ± 4.8 .785
 ASMI, kg/m2 26 7.2 ± 0.7 26 7.3 ± 0.9 .715
Biochemical parameters
 Fasting blood glucose, mg/dL 26 162.0 ± 36.1 26 157.5 ± 37.8 .430
 HbA1c, % 25 7.4 ± 0.8 25 7.2 ± 0.8 .190
 GA, % 21 19.7 ± 3.0 21 18.6 ± 2.4 .041
 TG, mg/dL 26 117.0 (77.3-148.5) 26 104.0 (73.8-128.8) .979
 HDL cholesterol, mg/dL 24 55.7 ± 17.3 24 56.3 ± 18.5 .580
 LDL cholesterol, mg/dL 23 113.6 ± 22.8 23 113.0 ± 20.9 .887
 AST, IU/L 26 24.0 (20.0-28.3) 26 22.0 (19.8-27.3) .091
 ALT, IU/L 26 19.5 (16.0-28.8) 26 22.5 (16.0-29.8) .298
 γ-GTP, IU/L 26 23.0 (17.0-32.8) 26 25.5 (16.0-32) .917
 eGFR, mL/min/1.73 m2 26 66.0 (53.5-82.9) 26 67.2 (54.6-86.7) .542
Abbreviations: ALT, alanine aminotransferase; ASMI, appendicular skeletal muscle index; ASMM, appendicular skeletal muscle mass; ASMR, appendicular skeletal muscle ratio; AST, aspartate transaminase; BMI, body mass index; eGFR, estimated glomerular filtration rate; GA, glycoalbumin; γ-GTP, γ-glutamyltransferase; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SMM, skeletal muscle mass; SMR, skeletal muscle ratio; TG, triglyceride.
aData are expressed as mean ± standard deviation or median (interquartile range). For the test of significance of the difference between the 2 groups, the Student t test was used when data were normally distributed, and the Mann-Whitney U test was used when data were not normally distributed. P values less than .05 were considered statistically significant.

The laboratory results of the participants indicated that the mean HbA1c level was not statistically significantly different between 2015 and 2016 (Table 1). The number of participants with HbA1c values of less than 7.0% increased from 8 (30.8%) in 2015 to 12 (46.2%) in 2016.

A correlation analysis performed on the 1-year variations in HbA1c level and each physical measurement showed that HbA1c level was significantly positively correlated with body weight, BMI, fat mass, and body fat percentage, and was significantly negatively correlated with SMR and ASMR (Table 2). The multiple regression analysis performed with HbA1c level as a dependent factor and the parameters that correlated with HbA1c level as independent factors showed a strong relationship only with SMR (β = −.589, P = .002). After the participants were categorized into those receiving insulin therapy (insulin therapy group, n = 16) and those not receiving insulin therapy (non–insulin therapy group, n = 10), multiple regression analyses, performed with HbA1c level as a dependent factor and independently correlated parameters, demonstrated similar results, showing only SMR to be strongly correlated. The insulin therapy group had a β coefficient of −.666 and P = .007, while the non–insulin therapy group had a β coefficient of −.738 and P = .015.

Table 2. - Correlation Between Changes in HbA1c and Those in Body Composition During 1 Yeara
Δ HbA1c
r P
Δ BMI 0.566 .003
Δ Body fat mass 0.531 .006
Δ Body fat ratio 0.556 .004
Δ Lean body mass −0.069 .745
Δ SMM −0.089 .674
Δ SMR −0.622 .001
Δ ASMM −0.035 .867
Δ ASMR −0.428 .033
Δ ASMI −0.025 .906
Abbreviations: Δ ASMI, change of appendicular skeletal muscle index; Δ ASMM, change of appendicular skeletal muscle mass; Δ ASMR, change of appendicular skeletal muscle ratio; Δ BMI, change of body mass index; ΔHbA1c, change of hemoglobin A1c; Δ SMM, change of skeletal muscle mass; Δ SMR, change of skeletal muscle ratio.
aIn the correlation analysis, Pearson correlation coefficient was used when data were normally distributed, and the Spearman rank correlation coefficient was used when data were not normally distributed. P values less than .05 were considered statistically significant.

Grip strengths could be measured for 9 men and 10 women. Analysis of the correlation between the grip strength and age, SMR, ASMR, and ASMI resulted in a lack of correlation (Table 3). After these parameters were classified according to the presence or absence of obesity and the correlations were analyzed, grip strength was positively correlated with SMR and ASMR among the obese women but not with ASMI (r = −0.513, P = .239). Appendicular skeletal muscle mass index showed correlations with body weight (men: r = 0.678, P = .045; women: r = 0.891, P = .001).

Table 3. - Correlation Between Grip Strength and SMR, ASMR, and ASMIa
All (n = 9) Nonobesity (n = 6) Obesity (n = 3)
r P r P r P
 Age −0.188 .628 −0.705 .118 −0.704 .502
 SMR 0.435 .241 0.189 .720 0.993 .075
 ASMR 0.274 .475 −0.380 .457 0.980 .128
 ASMI −0.030 .939 0.107 .841 0.313 .797
All (n = 10) Nonobesity (n = 3) Obesity (n = 7)
r P r P r P
 Age −0.401 .250 −0.749 .461 −0.181 .698
 SMR 0.322 .365 0.797 .412 0.918 .004
 ASMR 0.201 .579 0.563 .620 0.868 .011
 ASMI −0.121 .740 0.530 .644 −0.513 .239
Abbreviations: ASMI, appendicular skeletal muscle index; ASMR, appendicular skeletal muscle ratio; SMR, skeletal muscle ratio.
aIn the correlation analysis, Pearson correlation coefficient was used when data were normally distributed, and Spearman rank correlation coefficient was used when data were not normally distributed. P values less than .05 were considered statistically significant.

We evaluated the intake of energy, nutrients, and alcohol of participants using the BDHQ (Table 4). We examined the correlation between evaluated intake and HbA1c or SMR. A correlation analysis was performed on the 2015 survey results of the nutritional intake and 1-year variations in HbA1c level. It showed that each nutrient amount, (protein, animal protein, fat, vegetable oil, n-6 polyunsaturated fatty acids, total dietary fiber, soluble dietary fiber, and vitamin D), was negatively correlated with variations in HbA1c level shown in Table 5. Multiple regression analyses performed with the nutrients that correlated with HbA1c as independent factors showed only a strong relationship with soluble dietary fiber (β = −.644, P = .001).

Table 4. - Results of Survey Using BDHQ of Energy, Nutrients, and Alcohol Intake in 2015a
Sample (n = 26)
Energy, kcal/d 1848.5 ± 394.9
Protein, g/d 75.5 ± 21.5
 Animal protein, g/d 44.1 (34.0-54.0)
 Plant protein, g/d 29.3 (23.8-31.7)
Fat, g/d 57.6 ± 15.4
 Animal fat, g/d 26.8 ± 8.7
 Plant fat, g/d 30.8 ± 9.4
 SFA, g/d 14.2 ± 3.9
 MUFA, g/d 20.7 ± 5.9
 PUFA, g/d 15.0 ± 4.1
 n-6 PUFA, g/d 3.4 ± 1.1
 n-3 PUFA, g/d 11.5 ± 3.2
Carbohydrate, g/d 234.8 ± 46.4
Protein energy ratio, % 16.2 ± 2.1
Fat energy ratio, % 28.1 ± 4.8
Carbohydrate energy ratio, % 51.3 ± 6.0
Dietary fiber, g/d 12.0 (8.9-13.2)
 Water-soluble dietary fiber, g/d 3.0 ± 1.1
Vitamin D, μg/d 15.2 (11.7-23.1)
Alcohol, g/d 0.0 (0.0-8.5)
Abbreviations: MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid.
aData are expressed as mean ± standard deviation or median (interquartile range).

Table 5. - Correlation Between Nutrients Intake and Change in HbA1c and SMR Over 1 Yeara
r P r P
Energy −0.238 .252 0.253 .212
Protein −0.406 .044 0.370 .063
 Animal protein −0.399 .048 0.486 .012
 Plant protein −0.189 .367 −0.076 .710
Fat −0.400 .048 0.238 .242
 Animal fat −0.282 .172 0.274 .176
 Plant fat −0.398 .048 0.044 .830
 SFA −0.359 .078 0.246 .226
 MUFA −0.388 .055 0.232 .253
 PUFA −0.361 .076 0.257 .205
 n-6 PUFA −0.410 .042 0.363 .068
 n-3 PUFA −0.330 .107 0.212 .300
Carbohydrate −0.112 .594 0.203 .319
Protein energy ratio −0.411 .041 0.387 .051
Fat energy ratio −0.323 .116 0.067 .746
Carbohydrate energy ratio 0.211 .311 −0.199 .330
Dietary fiber −0.525 .007 0.333 .097
 Water soluble dietary fiber −0.550 .004 0.348 .082
Vitamin D −0.414 .040 0.421 .032
Alcohol −0.105 .616 0.234 .250
Abbreviations: ΔHbA1c, change of hemoglobin A1c; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; ΔSMR, change of skeletal muscle ratio.
aIn the correlation analysis, Pearson correlation coefficient was used when data were normally distributed, and Spearman rank correlation coefficient was used when data were not normally distributed. P values less than .05 were considered statistically significant.

A correlation analysis performed between the survey results of nutritional intake in the initial survey year and 1 year after showed variations in SMR. The intake amounts of animal protein and vitamin D were positively correlated with variations in SMR (Table 5). Multiple regression analyses performed with these nutrients as independent factors resulted in a strong correlation with animal protein intake amounts (β = .504, P = .009). After the participants were categorized into the insulin and non–insulin therapy groups, a multiple regression analysis was performed with SMR as a dependent factor and the parameters having correlation earlier as independent factors. The results showed that the insulin therapy group was related to the intake amount of vitamin D (β = .527, P = .036), and the non–insulin therapy group was related only to the intake amount of animal protein (β = .723, P = .018).


This study involved 26 subjects: 12 males and 14 females. We do not believe that this is large enough of a subject pool to apply our findings to the general population. This was an observational study that used patients with diabetes at 1 medical facility in Japan. However, among the findings on the evaluation of muscle, those that related to SMR seem logical. It was found that these are useful indicators for medical treatment that can be relatively easy and noninvasive in the clinical setting. In addition, 1 of the nutrition-related findings indicated that nutrient intake is beneficial to glycemic control and increases in the amount of muscle. From these findings, we think that the results of this study can serve as a clinical basis for further examination of diabetes therapies.

Multiple criteria for sarcopenia have been proposed in the literature; however, in Japan, the criteria reported by the Asian Working Group for Sarcopenia are used in consideration of racial and physical characteristics. There are currently no commonly recognized criteria for sarcopenia in T2DM patients with obesity. Half of the participants in this study were obese. Furthermore, the average BMI of T2DM patients in 2015 increased compared with that recorded 10 years earlier, according to a report by the Japan Diabetes Clinical Data Management Study Group.15 Moreover, the proportion of older adults with diabetes is increasing.15 Therefore, criteria for diagnosing sarcopenia in obese patients with T2DM are necessary for improving care.

Our findings revealed a strong relationship only with SMR. These findings were confirmed regardless of the presence of insulin therapy, representing the necessity to look at not only body weight or fat mass but also muscle mass to achieve an appropriate HbA1c concentration. Srikanthan and Karlamangla16 have shown that a 10% increase in skeletal muscle brings about an 11% reduction in HOMA-IR. Also in T2DM patients, maintenance of muscle can achieve good glycemic control.16 In addition, our results indicated the importance of not only increasing muscle mass but also maintaining it as much as possible when patients try to reduce fat mass.

In this study, ASMI was not correlated with grip strength, an index of muscle quality, for patients with and without obesity. Skeletal muscle mass ratio, which has often been used to determine sarcopenic obesity in previous reports,12 and ASMR, which is calculated by dividing the skeletal muscle mass only of the limbs by the body weight, were correlated positively with the grip strength of obese women in this study. A similar tendency was found among men, although it was not statistically significant (data not shown). Furthermore, even obese participants with lower grip strength who exhibited a positive correlation of body weight with the use of ASMI as an evaluation index demonstrated normal muscle mass. These participants were diagnosed with presarcopenia. Accordingly, it seems appropriate to use SMR or ASMR, rather than ASMI, as a quantitative index of muscle strength in the determination of at least sarcopenia in T2DM patients with obesity.

In this study, only SMR demonstrated usefulness as an evaluation index for treating T2DM patients with sarcopenia. We believe that differences in nutrient consumption over the first year of the study, at the very least, reflect relative differences in nutrient consumption among subjects. As a result, we have examined how variations in nutrient consumption over the first year influence the changes seen in each parameter. The 1-year variations in SMR were correlated with the intake amount of animal protein. Previous studies have shown that exercise and sufficient intake of certain nutrients, particularly amino acids, are effective in maintaining and increasing muscle mass.17 Anthony et al18 have reported that among amino acids, the branched-chain amino acids, including leucine, make up the muscle substrate. Branched-chain amino acids affect the activation of signals that induce muscle protein synthesis in combination with insulin.18 Because many branched-chain amino acids are contained in animal protein, the intake of protein sources such as fish or meat may contribute to the maintenance and increase in SMR. In addition, a relationship was found only between the insulin group and the amount of vitamin D. Bischoff-Ferrari et al19 have shown that the incidence of falls was reduced by 20% in a group receiving vitamin D. Chanet et al20 have shown that the vitamin D level in the blood of older adults was related to muscle mass, strength, and function. Also Verlaan et al21 have reported that supplementing breakfast with vitamin D and a medical nutrition drink with leucine-enriched whey protein stimulated postprandial muscle protein synthesis and increased muscle mass in healthy older adults. This may indicate a relationship between vitamin D intake and sarcopenia regarding both aspects of muscle quality and quantity.21 In this study, the 1-year variations in HbA1c level were negatively correlated with the intake amount of soluble dietary fiber. It has been reported that an intake of highly viscous, soluble dietary fiber effectively controls the increase in blood glucose levels of patients with diabetes.22 Based on these findings, attention should be paid not only to calories and carbohydrates but also to the appropriate consumption of protein, fiber, and vitamin D that may result in more effective therapy.

This study showed that SMR might be a potential index of sarcopenia in diabetic patients with obesity. Only SMR was associated with HbA1c. Thus, when treating patients with diabetes and sarcopenia, SMR seems to be a useful index. Regarding nutritional intake, this study also indicated a relationship between soluble dietary fiber and HbA1c level and between animal protein and vitamin D and the maintenance and increase in SMR. Few previous studies have included analyses of T2DM in a single report overtime with a series of factors such as muscle mass, glycemic control, and a nutrient intake analysis.


From the results, a clinical intervention study may be necessary that examines changes in SMR and HbA1c, following the implementation of a nutrition care plan that may be necessary. The care plan should pay careful attention to the consumption of animal protein and vitamin D and other nutrients beneficial in the treatment of diabetes. In addition, exercise has been supported by the efficacy of improving insulin sensitivity from various perspectives23 but was not addressed in this study. Appropriate dietary and exercise therapies aimed at maintaining and increasing SMR in T2DM patients could be conducted in a future study.


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nutrients; sarcopenia; skeletal muscle; type 2 diabetes

© 2019 The Authors. Published by Wolters Kluwer Health, Inc.