2021 年 68 巻 7 号 p. 781-789
Sarcopenia is associated with the risk of albuminuria in patients with type 2 diabetes mellitus (T2DM), and obesity is a risk factor for proteinuria. However, the association between sarcopenic obesity and diabetic nephropathy, including albuminuria, in patients with T2DM has not been reported. The study included 206 men and 163 women with T2DM who participated in the KAMOGAWA-DM cohort, which investigating the natural history of diabetes since 2014. Sarcopenia was defined as having both low skeletal muscle mass index (SMI, kg/m2) (<7.0 kg/m2 for men and <5.7 kg/m2 for women) and low handgrip strength (<28 kg for men and <18 kg for women). Obesity was diagnosed by the percentage of body fat (>30% for men and >35% for women). The patient was said to have sarcopenic obesity if he/she had both sarcopenia and obesity. Urinary albumin excretion of patients with sarcopenic obesity was higher than that of patients without sarcopenic obesity (median [interquartile range]: 342.0 [41.8–467.5] vs. 21.0 [9.0–75.4] mg/g Cr, p = 0.016). Additionally, sarcopenic obesity was associated with the presence of macroalbuminuria, compared with non-sarcopenic obesity (adjusted odds ratio 6.92 [95% confidence interval:1.63–29.4], p = 0.009). Adjusted odds ratios of sarcopenic obesity, sarcopenia only, and obesity only for the presence of macroalbuminuria were 6.52 (1.47–28.8, p = 0.014), 1.29 (0.45–3.71, p = 0.638), and 0.78 (0.38–1.58, p = 0.482), respectively, compared with neither sarcopenia nor obesity. This study indicated that sarcopenic obesity is associated with albuminuria, especially macroalbuminuria, in Japanese patients with T2DM.
NUMBER OF PATIENTS, especially elderly patients, with type 2 diabetes mellitus (T2DM) is increasing globally and they often develop sarcopenia [1]. Sarcopenia is defined as the age-associated decline of muscle strength, mass, and function [2]. Sarcopenia is now known as a risk factor for cardiovascular disease (CVD) and mortality [3-5]. Additionally, it has been reported that the presence of obesity in combination with sarcopenia, called as sarcopenic obesity, is a greater risk factor for mortality than the sarcopenia alone [6, 7]. It has been reported that there is a possibility that T2DM has a high risk of sarcopenic obesity [8, 9].
The prevalence of diabetic nephropathy, which is the major cause of end stage renal disease (ESRD), is also globally increasing [10]. Albuminuria, especially macroalbuminuria, is an independent risk factor not only for incident ESRD, [11-13] but also for mortality [14], particularly in patients with diabetes. Recent studies have showed that sarcopenia is associated with the risk of albuminuria in patients with T2DM [15, 16], and obesity is a risk factor for proteinuria [17]. Thus, there is a possibility that sarcopenic obesity is associated with albuminuria in patients with T2DM. However, there has not been a study that explored this association. Therefore, the present cross-sectional study researched the relationship between sarcopenic obesity and diabetic nephropathy, including the presence of macroalbuminuria, in patients with T2DM.
The KAMOGAWA-DM cohort study is an ongoing cohort study to elucidate the natural history of diabetes [18]. This study has been carried out since 2014. Patients enrolled in this study provided written informed consent. The present study is a part of KAMOGAWA-DM study. It includes the outpatients visiting the Department of Endocrinology and Metabolism, Kyoto Prefectural University of Medicine (KPUM) Hospital (Kyoto, Japan), and the Department of Diabetology, Kameoka Municipal Hospital (Kameoka, Japan). The present study included patients with T2DM who underwent bioimpedance analysis (BIA) between 15 January 2016 and 17 April 2018 [19]. The patients with unreliable BIA data, lacking urinary albumin excretion, and with poor handgrip strength were excluded from the study. This study was approved by the KPUM Ethics Committee (approval number RBMR-E-466-6) and has been conducted in accordance with the principles of Declaration of Helsinki.
Data collectionWe gave a standardized questionnaire to all participants, and categorized them as smokers or non-smokers, and non-exercisers or regular exercisers based on their responses in the questionnaire. Further, venous blood was gathered from the participants who had fasted overnight and the levels of triglycerides, high-density lipoprotein (HDL) cholesterol, creatinine (Cr), uric acid, and fasting plasma glucose were measured. Estimated glomerular filtration rate (eGFR) was calculated using the equation of the Japanese Society of Nephrology, i.e., eGFR = 194 × Cr–1.094 × age–0.287 (mL/min/1.73 m2) (×0.739, if woman) [20]. The hemoglobin A1c (HbA1c) level was estimated using high-performance liquid chromatography and was expressed as a National Glycohemoglobin Standardization Program unit. Further, the immunoturbidimetric method was used for the evaluation of urinary albumin excretion (UAE), which was calculated using the formula: UAE (mg/g Cr) = urinary albumin concentration (mg/L)/urinary Cr concentration (g/L). The mean UAE value was estimated from three independent readings. The stage of diabetic nephropathy was defined as follows: stage 1, eGFR ≥30 mL/min/1.73 m2 and normoalbuminuria (UAE <30 mg/g Cr); stage 2, eGFR ≥30 mL/min/1.73 m2 and microalbuminuria (UAE 30–300 mg/g Cr); stage 3, eGFR ≥30 mL/min/1.73 m2 and macroalbuminuria (UAE >300 mg/g Cr); and stage 4, eGFR <30 mL/min/1.73 m2 [21]. None of the patients required maintenance dialysis during this study. Additionally, the data for medications, including those for diabetes and hypertension (renin-angiotensin-aldosterone [RAS] inhibitor), and dyslipidemia (statin) were obtained. Data for medications were obtained from the patients’ medical records.
Body composition was evaluated using a multifrequency impedance body composition analyzer, InBody 720 (InBody Japan, Tokyo, Japan) [19]. The data for body weight (kg), appendicular muscle mass (kg), and body fat mass (kg) were obtained. Body mass index (BMI, kg/m2) and skeletal muscle mass index (SMI, kg/m2) were calculated by dividing body weight (kg) and appendicular muscle mass (kg), respectively, with the square of height (m). The percent body fat mass (%) was calculated by dividing body fat mass (kg) (×100) with body weight (kg). The handgrip strength was measured twice for each hand using a handgrip dynamometer (Smedley, Takei Scientific Instruments Co., Ltd., Niigata, Japan) and the maximum value was included in the analyses [19]. Sarcopenia was diagnosed by low SMI and low handgrip strength [22]. The cut-off values for low handgrip strength were <28 kg/m2 for men and <18 kg/m2 for women, and those for low SMI were <7.0 kg/m2 for men and <5.7 kg/m2 for women [22]. Obesity was defined by the percentage of body fat, i.e., >30% for men and >35% for women [23]. The patient was said to have sarcopenic obesity if he/she had both sarcopenia and obesity [23].
Statistical analysesThe data are showed as median (1st quartile-3rd quartile median), mean (standard deviation [SD]), or frequencies of potential confounding variables. Patients were divided into the following four groups based on the absence or presence of sarcopenia and obesity: absence of both sarcopenia and obesity (–/–), sarcopenia only (+/–), obesity only (–/+), and sarcopenic obesity (+/+). The differences in the continuous variables were evaluated by the Mann-Whitney U test, Kruskal-Wallis test and Steel-Dwass test, and one-way analysis of variance (ANOVA) and the Tukey-Kramer test, and those in the categorical variables were evaluated by the Chi-square test and the Holm test.
Further, we evaluated the association between sarcopenia and/or obesity, and albuminuria. Because UAE was a skewed variable, logarithmic transformation was done before performing multivariable linear regression analyses, which were performed to evaluate the association of combined effect of sarcopenia and/or obesity with loge (UAE + 1). Sex, age, smoking habit, exercise, BMI, systolic blood pressure, levels of HbA1c, uric acid, creatinine, duration of diabetes, consumption of RAS inhibitor, insulin, SGLT2 inhibitors, GLP-1 receptor agonists and statin were used for covariates.
Furthermore, we evaluated the effect of the presence of sarcopenia, obesity, and sarcopenic obesity on the presence of macroalbuminuria. Since the number of patients with macroalbuminuria was less, the propensity score, which preserves the statistical power by reducing covariates into a single variable, was used. The propensity score was calculated from multivariable logistic regression models that included age, sex, exercise, RAS inhibitor, insulin, SGLT2 inhibitors, GLP-1 receptor agonists, statin, duration of diabetes, family history of diabetes, smoking, systolic blood pressure, BMI, HbA1c, uric acid and creatinine. The c-statistic for propensity score model was 0.89, which indicates an acceptable discrimination between the presence or absence of macroalbuminuria. Further, the odds ratios of sarcopenia, obesity, and sarcopenic obesity for the presence of macroalbuminuria were calculated using the propensity score. The odds ratios of the presence or absence of sarcopenia or obesity for the presence of macroalbuminuria were also calculated.
In addition, we also performed sub-analyses of using the cut-off of obesity of BMI ≥25 kg/m2 [24] and evaluated the association between sarcopenia and/or obesity, and albuminuria, and the effect of the presence of sarcopenic obesity on the presence of macroalbuminuria.
The statistical analyses were conducted using JMP software ver. 13.2 (SAS Institute Inc., Cary, NC, USA) and EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan) [25], which is a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria). The differences with p value <0.05 were considered statistically significant.
In the present study, 383 patients (212 men and 171 women) with T2DM were extracted. Among them, 8 patients (5 men and 3 women) who had not undergone the multifrequency impedance body composition analyzer test, 4 patients (women) who had not undergone the handgrip strength test, and 2 patients with no date of UAE (1 man and 1 woman) were excluded from the study (Fig. 1). Finally, the study population comprised 369 patients.
Study flow diagram for the registration of patients.
UAE, urinary albumin excretion.
The clinical characteristics of the participants based on the absence or presence of sarcopenia and/or obesity are shown in Table 1. Mean age of patients with non-sarcopenia/non-obesity, sarcopenia only, obesity only, and sarcopenic obesity was 67.0 (8.9), 75.0 (8.4), 63.2 (12.3), and 75.4 (10.0) years, respectively. The percentage of patients with sarcopenia only, obesity only, and sarcopenic obesity was 7.9% (n = 29/369), 33.1% (n = 122/369), and 2.7% (n = 10/369), respectively. The patients with sarcopenic obesity were significantly older than those with obesity only. The BMI of patients with sarcopenic obesity was higher than those with sarcopenia alone, and lower than those with obesity alone. The proportion of patients using SGLT2 inhibitors or GLP-1 receptor agonists was lower in patients with sarcopenia only than those with neither sarcopenia nor obesity. Fig. 2 shows the proportion of diabetic nephropathy based on the stages according to the absence or presence of sarcopenia and/or obesity. The sarcopenic obesity was associated with the higher stage of diabetic nephropathy (p < 0.001).
| Sarcopenia/obesity | (–/–) | (+/–) | (–/+) | (+/+) | p |
|---|---|---|---|---|---|
| Men/Women | 132/76 | 22/7 | 45/77 | 7/3 | <0.001 |
| Age, years | 67.0 (8.9) | 75.0 (8.4)* | 63.2 (12.3)*† | 75.4 (10.0)‡ | <0.001 |
| Duration of diabetes, years | 14.5 (10.1) | 23.7 (10.8)* | 10.6 (8.0)*† | 15.0 (10.5) | <0.001 |
| Family history of diabetes (–/+) | 109/99 | 20/9 | 65/57 | 8/2 | 0.138 |
| Height, cm | 163.0 (8.8) | 159.2 (6.4) | 158.3 (9.5)* | 157.5 (7.8) | <0.001 |
| Body weight, kg | 60.5 (10.1) | 51.8 (6.6)* | 72.0 (13.7)*† | 62.5 (6.7)† | <0.001 |
| Body mass index, kg/m2 | 22.7 (2.5) | 20.4 (1.8)* | 28.7 (4.5)*† | 25.3 (2.6)†‡ | <0.001 |
| Systolic blood pressure, mmHg | 133.5 (19.7) | 132.2 (15.2) | 135.3 (16.7) | 132.1 (25.0) | 0.781 |
| Diastolic blood pressure, mmHg | 78.4 (11.1) | 72.0 (11.1)* | 81.8 (10.1)*† | 74.8 (14.0) | <0.001 |
| Insulin (–/+) | 153/55 | 18/11 | 101/21 | 9/1 | 0.047 |
| SGLT2 inhibitors (–/+) | 184/24 | 27/2* | 87/35 | 9/1 | <0.001 |
| GLP-1 receptor agonists (–/+) | 185/23 | 26/3* | 90/32 | 8/2 | 0.003 |
| RAS inhibitor (–/+) | 119/89 | 13/16 | 66/56 | 6/4 | 0.625 |
| Statin (–/+) | 129/79 | 18/11 | 75/47 | 7/3 | 0.963 |
| Smoking (–/+) | 173/35 | 26/3 | 106/16 | 10/0 | 0.367 |
| Habitual alcohol consumption (–/+) | 181/27 | 27/2 | 112/10 | 10/0 | 0.308 |
| Habit of exercise (–/+) | 108/100 | 15/14 | 65/57 | 4/6 | 0.883 |
| Hemoglobin A1c, % | 7.3 (1.2) | 7.4 (1.0) | 7.5 (1.5) | 7.3 (0.7) | 0.757 |
| Hemoglobin A1c, mmol/mol | 56.4 (13.4) | 57.7 (11.1) | 58.2 (16.1) | 56.7 (7.8) | 0.757 |
| Plasma glucose, mmol/L | 8.2 (2.7) | 8.6 (2.2) | 8.3 (3.0) | 7.6 (1.8) | 0.811 |
| Creatinine, umol/L | 74.8 (31.9) | 69.6 (19.4) | 69.2 (26.4) | 88.1 (47.5) | 0.128 |
| eGFR, mL/min/1.73 m2 | 68.6 (17.2) | 73.6 (22.1) | 71.5 (20.3) | 63.2 (28.5) | 0.255 |
| Uric acid, mmol/L | 301.5 (76.3) | 300.5 (76.7) | 311.6 (69.4) | 362.2 (85.2) | 0.064 |
| TG, mmol/L | 1.4 (0.9) | 1.1 (0.6) | 1.8 (1.0)*† | 1.5 (0.7) | <0.001 |
| HDL cholesterol, mmol/L | 1.6 (0.5) | 1.7 (0.5) | 1.5 (0.4) | 1.4 (0.3) | 0.029 |
| Urinary albumin excretion, mg/gCr | 20.0 (8.0–70.0) | 19.8 (12.0–110.9) | 27.1 (11.0–79.2) | 342.0 (41.8–467.5) | 0.028 |
| Diabetic nephropathy stage (1/2/3/4) | 125/56/24/3 | 16/8/5/0 | 68/40/10/4 | 2/1/5/2*†‡ | <0.001 |
| Handgrip strength, kg | 29.8 (9.0) | 21.8 (5.6)* | 27.2 (9.5)† | 19.6 (5.2)*‡ | <0.001 |
| Appendicular muscle mass, kg | 18.9 (4.1) | 15.7 (2.8)* | 18.1 (4.3)† | 15.5 (2.7)* | <0.001 |
| Body fat mass, kg | 14.8 (4.3) | 12.4 (3.6) | 28.3 (8.0)*† | 23.6 (4.6)*† | <0.001 |
| Percent body fat mass, % | 24.4 (5.9) | 23.6 (5.4) | 39.1 (6.0)*† | 37.8 (6.3)*† | <0.001 |
| SMI, kg/m2 | 7.0 (1.0) | 6.2 (0.7)* | 7.1 (1.1)† | 6.2 (0.6)*‡ | <0.001 |
Data was expressed as mean (standard deviation), median (interquartile range) or number. The difference between group was evaluated by ANOVA, Kruskal-Wallis test or chi-square test. RAS, renin-angiotensin system; eGFR, estimated glomerular filtration rate; TG, triglycerides; HDL, high-density lipoprotein; SMI, skeletal muscle mass index. *, p < 0.05 vs. (–/–); †, p < 0.05 vs. (+/–); and ‡, p < 0.05 vs. (–/+).
Proportion of diabetic nephropathy stage according to the presence/absence of sarcopenia and obesity
Urinary albumin excretion of patients with sarcopenic obesity was higher than that of patients without sarcopenic obesity (median [interquartile range]: 342.0 [41.8–467.5] vs. 21.0 [9.0–75.4] mg/g Cr, p = 0.016). Table 2 shows the association of combined effect of sarcopenia and/or obesity with loge (UAE + 1). The patients with sarcopenic obesity had significantly higher loge (UAE + 1) than those with neither sarcopenia nor obesity.
| Sarcopenia/obesity | (–/–) | (+/–) | (–/+) | (+/+) | p |
|---|---|---|---|---|---|
| Model 1 | 3.36 (3.14–3.59) | 3.68 (3.08–4.28) | 3.56 (3.27–3.85) | 4.99 (3.97–6.01)*† | 0.018 |
| Model 2 | 3.31 (3.09–3.54) | 3.40 (2.78–4.01) | 3.68 (3.38–3.98) | 4.72 (3.70–5.74)* | 0.022 |
| Model 3 | 3.63 (3.28–3.98) | 3.85 (3.22–4.48) | 3.88 (3.53–4.23) | 5.03 (4.09–5.97)* | <0.001 |
Values for outcome variables are geometric means and 95% CI. Log, logarithms.
Model 1 was unadjusted model, model 2 was adjusted for age and sex and model 3 was adjusted for age, sex, smoking, exercise, duration of diabetes, renin-angiotensin system inhibitor, insulin, SGLT2 inhibitors, GLP-1 receptor agonists, statin, systolic blood pressure, body mass index, HbA1c, uric acid, creatinine. *, p < 0.05 vs. (–/–); and †, p < 0.05 vs. (–/+).
Furthermore, the sarcopenic obesity was associated with the presence of macroalbuminuria after adjusting for covariates (odds ratio 6.92 [95% confidence interval: 1.63–29.4], p = 0.009). Compared to the non-sarcopenia/non-obesity patients, the patients with sarcopenic obesity had the highest risk for macroalbuminuria (OR 6.52 [95% CI: 1.47–28.80], p = 0.014; Table 3). Further, the adjusted odds ratios of sarcopenic obesity, sarcopenia only, and obesity only for the presence of macroalbuminuria were 6.52 (95% CI: 1.47–28.8, p = 0.014), 1.29 (95% CI: 0.45–3.71, p = 0.638), and 0.78 (95% CI: 0.38–1.58, p = 0.482), respectively. Additionally, the patients with sarcopenic obesity were at higher risk of macroalbuminuria compared to the patients with sarcopenia only (OR 5.06 [95% CI: 0.94–27.2], p = 0.059) and obesity only (OR 8.41 [95% CI: 1.85–38.2], p = 0.006).
| OR (95%CI) | p | |
|---|---|---|
| Absence of sarcopenia | ref | — |
| Presence of sarcopenia | 2.23 (0.97–5.09) | 0.058 |
| Absence of obesity | ref | — |
| Presence of obesity | 0.94 (0.50–1.78) | 0.894 |
| Absence of sarcopenic obesity | ref | — |
| Presence of sarcopenic obesity | 6.92 (1.63–29.4) | 0.009 |
| Presence/absence of sarcopenia and obesity | ||
| (–/–) | ref | — |
| (+/–) | 1.29 (0.45–3.71) | 0.638 |
| (–/+) | 0.78 (0.38–1.58) | 0.482 |
| (+/+) | 6.52 (1.47–28.8) | 0.014 |
Since the cases of macroalbuminuria is not enough. Propensity score was used for covariates. Propensity score was evaluated by multivariable logistic regression models that include the age, sex, duration of diabetes, family history of diabetes, smoking, exercise, renin-angiotensin system inhibitor, insulin, SGLT2 inhibitors, GLP-1 receptor agonists, statin, systolic blood pressure, body mass index, HbA1c, uric acid, and creatinine. The c-statistic for propensity score model was 0.89.
The results of sub-analysis of the association between sarcopenia and/or obesity, and loge (UAE + 1), using the cut-off of obesity of BMI ≥25 kg/m2, are show in Table 4. The proportion of sarcopenia alone, obesity alone and sarcopenic obesity were 9.2% (n = 34), 36.3% (n = 134) and 1.4% (n = 5), respectively. The patients with sarcopenic obesity had significantly higher loge (UAE + 1) than those with neither sarcopenia nor obesity. Moreover, the sarcopenic obesity, using the cut-off of obesity of BMI ≥25 kg/m2, was associated with the presence of macroalbuminuria after adjusting for covariates (OR 19.90 [95% CI: 2.03–194.00], p = 0.010) (Table 5).
| Sarcopenia/obesity | (–/–) | (+/–) | (–/+) | (+/+) | p |
|---|---|---|---|---|---|
| Model 1 | 3.31 (3.08–3.54) | 3.79 (3.24–4.34) | 3.62 (3.35–3.90) | 5.53 (4.09–6.96)* | 0.007 |
| Model 2 | 3.25 (3.03–3.48) | 3.49 (2.92–4.06) | 3.75 (3.46–4.03)* | 5.28 (3.85–6.70)* | 0.004 |
| Model 3 | 3.57 (3.21–3.92) | 3.91 (3.32–4.51) | 3.88 (3.55–4.22) | 5.44 (4.17–6.71)* | <0.001 |
Values for outcome variables are geometric means and 95% CI. Log, logarithms. Obesity was defined as BMI ≥25 kg/m2.
Model 1 was unadjusted model, model 2 was adjusted for age and sex and model 3 was adjusted for age, sex, smoking, exercise, duration of diabetes, renin-angiotensin system inhibitor, insulin, SGLT2 inhibitors, GLP-1 receptor agonists, statin, systolic blood pressure, body mass index, HbA1c, uric acid, creatinine. *, p < 0.05 vs. (–/–).
| Presence/absence of sarcopenia and obesity | OR (95%CI) | p |
|---|---|---|
| (–/–) | ref | — |
| (+/–) | 1.41 (0.52–3.78) | 0.498 |
| (–/+) | 0.85 (0.43–1.68) | 0.632 |
| (+/+) | 19.90 (2.03–194.00) | 0.010 |
Since the cases of macroalbuminuria is not enough. Propensity score was used for covariates. Propensity score was evaluated by multivariable logistic regression models that include the age, sex, duration of diabetes, family history of diabetes, smoking, exercise, renin-angiotensin system inhibitor, insulin, SGLT2 inhibitors, GLP-1 receptor agonists, statin, systolic blood pressure, body mass index, HbA1c, uric acid, and creatinine. The c-statistic for propensity score model was 0.89.
The present study researched the relationship between sarcopenic obesity and albuminuria in patients with T2DM. The proportion of patients with sarcopenia only and sarcopenic obesity were 7.9% and 2.7%, respectively. These proportions were similar to the previous studies reported [26, 27]. The UAE of patients with sarcopenic obesity was higher than that of patients neither with sarcopenia and obesity. Additionally, sarcopenic obesity was associated with the presence of macroalbuminuria in patients with T2DM.
Sarcopenia has been established as a risk factor for CVD and mortality [3-5]. Additionally, it is known to be a risk of albuminuria [15, 16]. Sarcopenic obesity, which more likely causes lifestyle-related morbidities than obesity without sarcopenia, may lead to a further reduction in mobility and is associated with higher mortality rates than sarcopenia alone [6, 7]. Moreover, a recent study showed that sarcopenic obesity had a higher risk of albuminuria than sarcopenia [28]. However, the definition of sarcopenia used in that study was SMI (%), which is the appendicular muscle mass (kg) divided by body weight (kg) × 100, which was different from SMI (kg/m2) used in our study. In fact, SMI (%) and SMI (kg/m2) are clinically different markers [29]. In the present study, the risk of albuminuria was the highest in the patients with sarcopenic obesity. Previous studies showed that both sarcopenia and obesity were the risk of macroalbuminuria [15-17]. On the other hand, in this study, we revealed that not the presence of sarcopenia only or obesity only but the sarcopenic obesity was a higher risk of macroalbuminuria. Thus, we should focus on sarcopenic obesity for a higher risk of macroalbuminuria, which are higher risk of renal dysfunction progression.
The diagnostic criteria for sarcopenic obesity, especially regarding obesity, are not unified yet [30, 31]. Percentage of body fat, BMI, and waist circumference has been used for definitions and diagnose for obesity [31]. In the present study, we used the cut-off values for percentage of body fat >35% in women and >30% in men for obesity [23]. A recent study has suggested that this criterion is suitable for the Japanese [23]. On the other hand, in the sub-analysis using the cut-off of obesity of BMI ≥25 kg/m2, sarcopenic obesity was associated with albuminuria. Therefore, regardless of which definition is used, sarcopenic obesity was associated with albuminuria.
To prevent muscle mass loss, exercise and protein intake are important [32]. On the other hand, in obesity, it is important to reduce total calorie intake for weight loss [33]. Thus, there is a possibility that limiting carbohydrates and fats, but not reducing protein, might be needed in patients sarcopenic obesity [34]. A previous study revealed that protein intake was not associated with progression of albuminuria in patients with T2DM without macroalbuminuria [35]. However, it remained to be unclear regarding protein intake in diabetic nephropathy; and thus, further research on the prevention and improvement of sarcopenic obesity is needed.
The possible link between sarcopenic obesity and albuminuria can be explained as follows. The loss of skeletal muscle has a close association with insulin resistance, reactive oxygen species, and chronic inflammation, which can, in turn, promote albuminuria. Sarcopenia has been reported to be associated with an inflammatory state driven by cytokines and oxidative stress [36]. In fact, sarcopenia has been reported to be associated with nuclear factor κB (NF-κB) and protein kinase B (Akt) signaling through secretion of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and transforming growth factor-β (TGF-β) [37]. Additionally, a previous study has revealed that obesity, like sarcopenia, increases albuminuria by triggering cascades of events including insulin resistance, increased reactive oxygen species, and chronic inflammation [38]. Obesity is also associated with Akt and NF-κB signaling through secretion of IL-6, TNF- α, and TGF-β. Insulin resistance, reactive oxygen species, and chronic inflammation have been reported to induce albuminuria [39]. Therefore, having both sarcopenia and obesity accelerates these insulin resistance and chronic inflammation through fat accumulation in the muscles and insufficient working of insulin. Taken together, the aforementioned mechanisms suggest that the synergistic effect of sarcopenia and obesity is associated with albuminuria.
This study has a few limitations. First, the study design was cross-sectional. Therefore, the causal relationship between the sarcopenic obesity and albuminuria is unclear. Second, the generalizability of the results of the present study in non-Japanese T2DM patients is unclear. Third, we did not have the data of insulin resistance, reactive oxygen species, and inflammation, which might be link the sarcopenic obesity and albuminuria. Fourth, the sample size of this study was relatively small. The future prospects are to examine longitudinal albuminuria changes in patients with T2DM with sarcopenic obesity, and to clarify the association between sarcopenic obesity and albuminuria.
In conclusion, the findings of the present study suggest an association between sarcopenic obesity and albuminuria, including macroalbuminuria, in Japanese patients with T2DM. Preventing sarcopenic obesity would reduce the risk of diabetic nephropathy, which is a major cause of ESRD in T2DM.
We would like to thank Editage (www.editage.jp) for English language editing.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
None.
Dr. Hashimoto reports grants from Asahi Kasei Pharma and personal fees from Sanofi K.K., Daiichi Sankyo Co. Ltd., Novo Nordisk Pharma Ltd., and Mitsubishi Tanabe Pharma Corp. outside the submitted work. Dr. Hamaguchi reports grants from Mitsubishi Tanabe Pharma Corp., Sanofi K.K., Asahi Kasei Pharma, Kyowa Kirin Co. Ltd., Eli Lilly Japan K.K., Astellas Pharma Inc., Sumitomo Dainippon Pharma Co. Ltd., Novo Nordisk Pharma Ltd., Takeda Pharma Co. Ltd, Nippon Boehringer Ingelheim Co. Ltd., and Daiichi Sankyo Co. Ltd. outside the submitted work. Prof. Fukui received grants from Taisho Pharma Co., Ltd., Ono Pharma Co. Ltd., Sumitomo Dainippon Pharma Co., Ltd., Nippon Boehringer Ingelheim Co. Ltd., Mitsubishi Tanabe Pharma Corp, Kissei Phama Co. Ltd., Takeda Pharma Co. Ltd., Daiichi Sankyo Co. Ltd., Sanofi K.K., MSD K.K., Sanwa Kagagu Kenkyusho CO., LtD., Astellas Pharma Inc., Kowa Pharma Co. Ltd., Abbott japan Co. Ltd., Kyowa Kirin Co., Ltd., Novo Nordisk Pharma Ltd., Eli Lilly Japan K.K., Tejin Pharma LtD., Nippon Chemiphar Co., Ltd., Johnson & Johnson k.k. Medical Co., and Terumo Corp., and received honoraria from Taisho Pharma Co., Ltd., Ono Pharma Co. Ltd., Bayer Yakuhin, Ltd., Mitsubishi Tanabe Pharma Corp., Sanwa Kagaku Kenkyusho Co. Ltd., Astellas Pharma Inc., Sanofi K.K., Takeda Pharma Co. Ltd., MSD K.K., Kyowa Kirin Co. Ltd., Daiichi Sankyo Co. Ltd., Novo Nordisk Pharma Ltd., Kowa Pharma Co. Ltd., Sumitomo Dainippon Pharma Co. Ltd., Nippon Boehringer Ingelheim Co., Ltd., Eli Lilly Japan K.K., AstraZeneca K.K., Mochida Pharma Co. Ltd., Abbott japan Co. Ltd., Medtronic Japan Co. Ltd., Teijin Pharma Ltd., Arkray Inc., Kissei Pharma Co., Ltd., and Nipro Corp. outside the submitted work. The other authors have nothing to disclose.
FT analyzed and interpreted the data, and wrote the manuscript. YH originated and designed the study, researched, analyzed and interpreted the data, and drafted the manuscript. AK and RS originated the study, researched and interpreted the data, and reviewed the manuscript. TO researched the data, and reviewed the manuscript. MH originated and designed the study, researched the data, and reviewed the manuscript. MF originated the study, researched and interpreted the data, and drafted the manuscript. All authors were involved in the writing of the manuscript and approved the final version of this article.
