Abstract
Background
This study aims to investigate the association between sarcopenia and chronic kidney disease (CKD) among diabetes mellitus (DM) patients.
Methods
The study used the data extracted from 1,855 patients collected by the Korean National Health and Nutrition Examination Surveys conducted in 2008–2011. Body mass index (BMI)-adjusted, weight-adjusted, and height2 -adjusted appendicular skeletal muscle mass were used to define sarcopenia, and the young reference group was analyzed for the cutoff values, which are 0.9 for men 0.6 for women in BMI-adjusted, 29.6 for men, and 23.3 for women in weight-adjusted, and 6.9 – men and 4.8 – women in height2-adjusted index. Participants were classified as having diabetes if they either presented with a fasting plasma glucose ≥126 mg/dL (7.00 mmol/L) or glycated hemoglobin ≥6.5% (48 mmol/moL) or provided any self-report of a physician’s diagnosis or current treatment of diabetes. The equation developed by the CKD Epidemiology Collaboration was used to calculate the estimated glomerular filtration rate (eGFR). CKD was defined as having an eGFR less than 60 mL/min/1.73 m2. The association between sarcopenia and CKD was assessed using multiple logistic regression, adjusting for potential confounders.
Results
The final analysis included 1,855 participants with diabetes. After adjusting for potential confounders, sarcopenia was significantly associated with an increased prevalence of CKD. Specifically, patients with sarcopenia had 1.826 times the odds of having CKD compared to those without sarcopenia (odds ratio, 1.826; 95% confidence interval, 1.092–3.054).
Conclusions
There is an association between sarcopenia and CKD in DM patients.
Keywords: Diabetes mellitus; Glomerular filtration rate; Muscle, Skeletal; Renal insufficiency, chronic; Sarcopenia
GRAPHICAL ABSTRACT
INTRODUCTION
Diabetic mellitus (DM) is a prevalent metabolic disorder posing a huge influence on millions worldwide, and its complications extend beyond glycemic control. Based on a report issued by the Centers for Disease Control and Prevention in 2021, 38.4 million people in the United States have diabetes, accounting for 11.6% of the total U.S. population. The percentage of patients increases with age, reaching 29.2% among those aged over 65.[1] Thirty-nine point two percent of U.S. adults aged 18 years or older with diagnosed diabetes had chronic kidney diseases (CKD) according to the updated 2021 CKD Epidemiology Collaboration (CKD-EPI). The percentage of end-stage kidney disease attributed to diabetes as the primary cause stood at 39.2%. Consequently, diabetes emerged as the primary contributor to end-stage kidney disease.[2]
Sarcopenia refers to a disease rooted in muscle changes in a negative way that accumulates over a lifetime. Following the criteria of sarcopenia defined by the European Working Group on Sarcopenia in Older People (EWGSOP), the prevalence increased rapidly with age, 31.6% of women and 17.4% of men aged over 80 were under this condition.[ 3] Although it is commonly diagnosed in elderly people, it can also occur early in life.[4] Intensive research efforts are concentrated on sarcopenia to translate existing insights into its pathophysiology into enhanced methods of diagnosis and treatment, especially with particular emphasis on advancing biomarkers.[5]
According to the World Health Organization, the global average life expectancy increased to 73.4 years in 2019. South Korea’s life expectancy at birth was 83.3 years in 2019, which was almost 10 years higher than the global average.[6] Additionally, South Korea is one of the nations with the fastest-aging population. It is expected to become a super-aged society by 2025, with individuals aged 65 years and older accounting for 20% of the total population.[ 7] The prevalence of DM, CKD, and sarcopenia is higher in the aging population with diseases, which make up almost 20% of the population in South Korea.[8,9] Under such circumstances, understanding the complex interaction among these conditions has been underscored.
Sarcopenia, DM, and CKD are distinct medical conditions, but there are multiple shared mechanisms.[10,11] The prevalence of sarcopenia is three times higher in individuals with diabetes compared to those without, and is associated with an unfavorable prognosis.[12] Moreover, sarcopenia is notably common among patients with CKD, particularly those undergoing dialysis, and is linked to increased mortality and functional decline, as evidenced by lower handgrip strength.[13,14] Several studies have confirmed an association between sarcopenia and markers of renal dysfunction, such as albuminuria in diabetic population.[ 15] While the pairwise relationships between these conditions are established, research focusing particularly on diabetic patients is still limited. This is a critical area of inquiry, with the recent study using a large-scale cohort study demonstrates the causation between CKD and sarcopenia.[16] Furthermore, few studies have investigated sex-specific differences in this association. This gap is significant as some studies suggest that the risk of higher sarcopenia status is associated with the aged male population.[ 17] Therefore, the objective of this study is to investigate the association between sarcopenia and CKD among DM patients using a large, nationally representative dataset, while also exploring potential sex-specific differences in this association.
METHODS
1. Overview of population
The population used in this study was extracted from the population-based, cross-sectional data collected by the Korean National Health and Nutrition Examination Survey (KNHANES) conducted in 2008–2011. KNHANES is the nationwide survey calculating the health level, health-related awareness and behavior, and food and nutrition consumption condition of every ten thousand people based upon the National Health Promotion Act.
Initially, 2,026 participants with DM were identified from the KNHANES 2008–2011. Participants were included in this diabetes cohort if they met at least one of the following criteria: (1) self-reported lifetime or current prevalence of diabetes; (2) a physician’s diagnosis of diabetes; (3) currently receiving treatments for diabetes; (4) a fasting plasma glucose (FPG) level ≥126 mg/dL, or a glycated hemoglobin (HbA1c) level ≥6.5%.
From this initial cohort, 171 participants with missing dual energy X-ray absorptiometry (DXA) data and anthropometric measures required to define sarcopenia, as well as those lacking serum creatinine data for the calculation of estimated glomerular filtration rate (eGFR) were excluded. This resulted in the final population of 1,855 respondents, as illustrated in Figure 1.
Fig. 1.
Flow diagram of inclusion and exclusion of subjects. DM, diabetes mellitus; KNHANES, Korean National Health and Nutrition Examination Survey.
2. Define and assess sarcopenia, DM, and CKD
Sarcopenia was defined based on body mass index (BMI)-adjusted, weight-adjusted, and height2-adjusted appendicular skeletal muscle mass (ASM) indices, calculated as ASM/BMI, (ASM/body weight [kg])×100, and ASM/height2, respectively. Participants were classified as sarcopenic if this index fell below the Class I cutoff of each index. ASM was calculated as the sum of muscle mass in both arms and legs, considering that all non-fat and nonbone tissue was skeletal muscle.[18]
The cutoff value was defined as one standard deviation below the mean of the young, sex-specific reference group. The young reference group includes healthy women and men aged between 20 and 39 without any history of clinical diseases, such as hypertension, hyperlipidemia, stroke, myocardial infarction, angina pectoris, osteoarthritis, rheumatoid arthritis, pulmonary tuberculosis, asthma, renal failure, diabetes, disorders of thyroid gland, liver cirrhosis, type B and C hepatitis, and depression. Also, any type of other cancers including lung cancer, stomach cancer, liver cancer, and large intestine cancer. Additionally, breast and cervical cancer in women. In total, 1,295 subjects remained (Table 1).
Table 1.
Characteristics of the young reference group (N=1,295)
| Characteristics | Men (N=587) | Women (N=708) |
|---|---|---|
| Age (yr) | 31.3±5.4 | 31.0±5.7 |
|
| ||
| Weight (kg) | 73.0±11.3 | 55.9±8.6 |
|
| ||
| Height (cm) | 174.0±5.5 | 160.3±5.7 |
|
| ||
| BMI (kg/m2) | 24.1±3.4 | 21.8±3.2 |
|
| ||
| ASM (kg) | 23.5±3.2 | 14.3±2.2 |
|
| ||
| (ASM/weight) ×100 | 32.5±2.9 | 25.6±2.3 |
|
| ||
| (ASM/height2) | 7.8±0.9 | 5.6±0.8 |
|
| ||
| ASM/BMI | 1.0±0.1 | 0.7±0.1 |
|
| ||
| Cutoff values | ||
| ASM/BMI | ||
| Class I sarcopenia | 0.9 | 0.6 |
| Class II sarcopenia | 0.8 | 0.5 |
| (ASM/weight)×100 | ||
| Class I sarcopenia | 29.6 | 23.3 |
| Class II sarcopenia | 26.7 | 21.0 |
| ASM/height2 | ||
| Class I sarcopenia | 6.9 | 4.8 |
| Class II sarcopenia | 5.9 | 4.0 |
All the values are rounded to the nearest tenth. Continuous variables are presented as mean±standard deviation (SD). Appendicular skeletal muscle mass (ASM) was calculated as the sum of the skeletal muscles of arms and legs. Participants classified as Class I and II sarcopenia are those whose body mass index (BMI)-adjusted, weight-adjusted, height2-adjusted ASM were between −1.0 to −2.0 SD or below −2.0 SD of the sex-specific young reference group.
DM refers to a metabolic disease characterized by chronic hyperglycemia resulting from dysfunction of insulin secretion, insulin action, or both.[19] The diagnosis of DM is established based on key glycemic markers recommended by the American Diabetes Association. The criteria include a FPG level of ≥126 mg/dL (7.00 mmol/L), HbA1c level of ≥6.5% (48 mmol/moL).[20] For the purposes of the present study, participants were operationally defined as having diabetes if they either met the biochemical criteria or provided any self-report of a physician’s diagnosis or current treatment of diabetes. Both biochemical markers and multiple self-reported indicators were incorporated to maximize the inclusion of all diabetes cases and enhance the representativeness of the study population.[21]
The eGFR is one of the best indices for assessing kidney function. The equation developed by the CKD-EPI was used to calculate eGFR.[22] Serum creatinine was measured by colorimetry using the Hitachi automatic analyzer 7600. Participants were defined as having CKD if they had an eGFR less than 60 mL/min/1.73 m2. Based on the 2012 Kidney Disease Improving Global Outcomes (KDIGO), the condition of CKD is classified into 6 categories: G3a, eGFR 45 to 59 mL/min/1.73 m2; G3b, GFR 30 to 44 mL/min/1.73 m2; G4, GFR 15 to 29 mL/min/1.73 m2; G5, GFR less than 15 mL/min/1.73 m2.[23] Due to the cross-sectional nature of the study, the chronicity criterion (duration over 3 months) required for a formal CKD diagnosis could not be applied.
3. Statistical analyses
All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), P-value of less than 0.05 was considered statistically significant. To ensure the results were representative of the Korean population, all analyses incorporated sampling weights that accounted for the complex sample design, non-response rates, and post-stratification. Descriptive statistics are presented as mean±standard error (SE) for normally distributed continuous variables and as frequencies and percentages for categorical variables, rounded to the nearest tenth. To investigate the independent association between sarcopenia and CKD in participants with DM, a series of multivariable logistic regression models were constructed, three sequentially adjusted models: Modela was adjusted for age and sex; Modelb was additionally adjusted for waist circumference, smoking status (never smoker, past smoker, current smoker), and regular walking status (at least 30 min/day, on over 5 days a week); and Modelc was further additionally adjusted total cholesterol, triglyceride, HbA1c, and hypertension. The results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). Multicollinearity among the covariates in the fully adjusted model was assessed using the variance inflation factor (VIF). A VIF value greater than 5 was considered to indicate significant multicollinearity, but the VIFs for all covariates in modelc were less than 2.2.
RESULTS
1. Characteristics of the study participants based on the presence of sarcopenia
A total of 1,855 participants from the KNHANES 2008- 2011 were included in this study, with baseline characteristics by how sarcopenia was defined as presented in Table 2. Based on the BMI-adjusted definition, 983 participants (53.1%) were classified as having sarcopenia, while 869 participants (46.9%) were not. When defined by (ASM/weight)×100 index, 623 participants (33.6%) were sarcopenic, and 1,230 (66.4%) were not. Lastly, 268 subjects (14.5%) had sarcopenia, and 1,586 (85.5%) did not, in height2-adjusted sarcopenia. It was found that the prevalence of sarcopenia varies by the methods of defining sarcopenia, and it aligns with the finding that the prevalence of sarcopenia and its relationship to clinical outcome differ according to the diagnostic criteria used for low lean mass. The ASM/height2 definition showed the lowest prevalence among the three indices.[24] Additionally, 180 participants (8.31%) were classified as having CKD among diabetes. Of these, the majority had Stage 3 CKD, with 126 (70.94%) in Stage 3a (eGFR 45–59) and 37 (20.3%) in Stage 3b (eGFR 30–44). The remaining participants were in Stage 4 (eGFR 15–29, N=12, 5.5%) and Stage 5 (eGFR <15, N=5, 3.3%).
Table 2.
Baseline characteristics of the subjects with the presence of sarcopenia
| Characteristics | Non-sarcopenia | Sarcopenia | P-value |
|---|---|---|---|
| ASM/BMI (N=1,852) | |||
| No. of patients | 869 | 983 | |
| Age (yr) | 54.4±0.5 | 61.5±0.5 | <0.0001 |
| Sex, male | 414 (54.8) | 511 (55.4) | 0.8362 |
| ASM (kg) | 20.0±0.2 | 17.1±0.2 | <0.0001 |
| Waist circumference (cm) | 85.9±0.4 | 88.9±0.4 | <0.0001 |
| BMI (kg/m2) | 24.4±0.1 | 25.9±0.1 | <0.0001 |
| Total cholesterol (mg/dL) | 190.5±1.5 | 190.4±1.5 | 0.9577 |
| Triglyceride (mg/dL) | 193.1±7.5 | 184.9±5.0 | 0.3732 |
| HbA1c | 7.4±0.1 | 7.3±0.1 | 0.1909 |
| eGFR, CKD-EPI (mL/min/1.73 m2) | 91.7±0.7 | 84.4±0.8 | <0.0001 |
| Hypertension | 445 (48.7) | 699 (68.3) | <0.0001 |
| Regular walker | 151 (19.8) | 181 (21.7) | 0.4470 |
| Smoking status | 0.0387 | ||
| Never smoker | 457 (47.0) | 50 (49.5) | |
| Past smoker | 91 (11.4) | 144 (15.2) | |
| Current smoker | 314 (41.6) | 317 (35.3) | |
|
| |||
| (ASM/weight)×100 (N=1,853) | |||
| No. of patients | 1,230 | 623 | |
| Age (yr) | 56.6±0.5 | 60.8±0.7 | <0.0001 |
| Sex, male | 623 (56.5) | 303 (52.1) | 0.1202 |
| ASM (kg) | 19.1±0.2 | 17.3±0.2 | <0.0001 |
| Waist circumference (cm) | 85.2±0.3 | 92.3±0.5 | <0.0001 |
| BMI (kg/m2) | 24.3±0.1 | 27.0±0.2 | <0.0001 |
| Total cholesterol (mg/dL) | 189.3±1.4 | 192.8±1.9 | 0.1519 |
| Triglyceride (mg/dL) | 188.7±6.0 | 189.3±5.5 | 0.9378 |
| HbA1c | 7.4±0.1 | 7.2±0.1 | 0.0175 |
| eGFR, CKD-EPI (mL/min/1.73 m2) | 89.7±0.6 | 84.6±1.1 | <0.0001 |
| Hypertension | 680 (52.0) | 464 (72.4) | <0.0001 |
| Regular walker | 218 (20.5) | 115 (21.5) | 0.7401 |
| Smoking status | 0.0297 | ||
| Never smoker | 630 (46.3) | 336 (52.3) | |
| Past smoker | 143 (12.7) | 92 (14.6) | |
| Current smoker | 442 (41.0) | 190 (33.2) | |
|
| |||
| ASM/height2 (N=1,854) | |||
| No. of patients | 1,586 | 268 | |
| Age (yr) | 57.5±0.4 | 61.2±0.9 | 0.0005 |
| Sex, male | 694 (50.1) | 232 (87.7) | <0.0001 |
| ASM (kg) | 18.8±0.1 | 16.8±0.2 | <0.0001 |
| Waist circumference (cm) | 88.4±0.3 | 81.3±0.7 | <0.0001 |
| BMI (kg/m2) | 25.7±0.1 | 21.6±0.2 | <0.0001 |
| Total cholesterol (mg/dL) | 191.9±1.1 | 181.4±3.5 | 0.0056 |
| Triglyceride (mg/dL) | 191.4±4.8 | 174.07±12.0 | 0.2013 |
| HbA1c | 7.3±0.1 | 7.4±0.1 | 0.7792 |
| eGFR, CKD-EPI (mL/min/1.73 m2) | 88.5±0.6 | 85.4±1.6 | 0.0781 |
| Hypertension | 991 (59.1) | 154 (55.0) | 0.3320 |
| Regular walker | 278 (20.1) | 54 (24.8) | 0.2155 |
| Smoking status | <0.0001 | ||
| Never smoker | 897 (52.1) | 71 (23.1) | |
| Past smoker | 176 (11.4) | 59 (25.7) | |
| Current smoker | 499 (36.5) | 132 (51.2) | |
All the analyses were performed using sampling weights and the values are rounded to the nearest tenth. Continuous variables are presented as mean±standard error and categorical variables are presented as N (%). Appendicular skeletal muscle (ASM) was calculated as the sum of the skeletal muscles of arms and legs. Regular walker was defined as waling for more than 30 min per time and more than five days a week.
BMI, body mass index; HbA1c, glycated hemoglobin; eGFR, estimated glomerular filtration rate; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration.
Across all three definitions, participants with sarcopenia were significantly older and had significantly lower ASM than the non-sarcopenic group. A notable difference emerged in sex distribution: while the sex ratio was relatively balanced in the /BMI (Male, 55.4%) and /weight (Male, 52.1%) definitions, the sarcopenic group in the /height2 definition was predominantly male (87.7%, P<0.0001). In terms of anthropometric measures, the sarcopenic group showed significantly higher waist circumference and BMI in the /BMI and /weight definitions (P<0.0001). Conversely, the opposite trend was observed in the /height2 definition. Among biochemical markers, Triglyceride levels showed no statistically significant differences in any definition. For total cholesterol, the sarcopenic group had significantly lower levels only in the /height2 definition (P=0.0056). For HbA1c, the non-sarcopenic group had significantly higher levels only in the /weight definition (P=0.0175). Similarly, mean eGFR was significantly lower in the sarcopenic groups for the /BMI (P<0.0001) and /weight (P<0.0001) definitions, but this difference was not statistically significant for the /height2 definition (P=0.0781). The prevalence of hypertension was significantly higher in the sarcopenia groups defined by /BMI (P<0.0001) and /weight (P<0.0001). In contrast, no significant difference was observed in the /height2 definition (P=0.3320). Regarding lifestyle factors, the ratio of regular walkers did not differ significantly between groups in any definition. However, the distribution of smoking status differed significantly across all three definitions (P=0.0387, P=0.0297, P<0.0001, respectively).
2. Sarcopenia is associated with the increased odds of CKD in patients with DM
Table 3 summarizes the association between sarcopenia and CKD in the diabetic population by different definitions of sarcopenia. When sarcopenia was defined by ASM/BMI, the sarcopenic group had 3.349 times the odds of having CKD in the crude model (OR, 3.349; 95% CI, 2.274–4.933). This association remained statistically significant across all subsequent adjustment models. After adjusting for age and sex (Modela), the OR was 1.969 (95% CI, 1.286–3.012). Further adjustment for waist circumference, smoking status, and regular walker (Modelb) yielded an OR of 2.129 (95% CI, 1.278–3.547). In the fully adjusted model (Modelc), which additionally controlled for hypertension, total cholesterol, triglyceride, and HbA1c, sarcopenia remained significantly associated with CKD (OR, 1.826; 95% CI, 1.092–3.054).
Table 3.
Crude and adjusted odds ratios and 95% confidence intervals for the association between chronic kidney disease among diabetes mellitus patients
| Variables | Non-sarcopenia | Sarcopenia | P-value |
|---|---|---|---|
| ASM/BMI | |||
| No. of patients | 869 | 983 | |
| Crude model (N=1,852) | 1.000 (Ref) | 3.349 (2.274–4.933) | <0.0001 |
| Modela (N=1,852) | - | 1.969 (1.286–3.012) | 0.0019 |
| Modelb (N=1,517) | - | 2.129 (1.278–3.547) | 0.0038 |
| Modelc (N=1,423) | - | 1.826 (1.092–3.054) | 0.0218 |
|
| |||
| (ASM/weight)×100 | |||
| No. of patients | 1,230 | 623 | |
| Crude model (N=1,853) | 1.000 (Ref) | 2.269 (1.565–3.290) | <0.0001 |
| Modela (N=1,853) | - | 1.656 (1.122–2.444) | 0.0111 |
| Modelb (N=1,518) | - | 1.887 (1.179–3.020) | 0.0083 |
| Modelc (N=1,424) | - | 1.638 (0.997–2.693) | 0.0515 |
|
| |||
| ASM/height2 | |||
| No. of patients | 1,586 | 268 | |
| Crude model (N=1,854) | 1.000 (Ref) | 1.871 (1.195–2.930) | 0.0063 |
| Modela (N=1,854) | - | 0.919 (0.519–1.627) | 0.7708 |
| Modelb (N=1,517) | - | 0.832 (0.406–1.708) | 0.6162 |
| Modelc (N=1,423) | - | 0.836 (0.412–1.694) | 0.6178 |
All the analyses were performed using sampling weights. Modela was adjusted for age and sex. Modelb was adjusted for age, sex, waist circumference, smoking status, and regular walker. Modelc was adjusted for age, sex, waist circumference, smoking status, regular walker, hypertension, total cholesterol, triglyceride, and glycated hemoglobin.
ASM, appendicular skeletal muscle mass; BMI, body mass index; Ref, reference.
Using the (ASM/weight)×100 definition, a significant association was observed in the crude model (OR, 2.269; 95% CI, 1.565–3.290), Modela (OR, 1.656; 95% CI, 1.122–2.444), and Modelb (OR, 1.887; 95% CI, 1.179–3.020). However, after full adjustment in Modelc, the association was no longer statistically significant (OR, 1.638; 95% CI, 0.997–2.693).
When sarcopenia was defined by ASM/height2, a significant association was found in the crude model (OR, 1.871; 95% CI, 1.195–2.930), but disappeared after adjusting for age and sex in Modela (OR, 0.919; 95% CI, 0.519–1.627). This lack of association persisted in Modelb (OR, 0.832; 95% CI, 0.406–1.708) and the fully adjusted Modelc (OR, 0.836; 95% CI, 0.412–1.694).
3. Sex-specific association between sarcopenia and CKD in DM population
To examine potential differences by sex, the association between sarcopenia and CKD was analyzed separately for men and women using three sarcopenia definitions. The results are detailed in Table 4.
Table 4.
Sex-specific odds ratios and 95% confidence intervals for the association between chronic kidney disease among diabetes mellitus patients
| Variables | Non-sarcopenia (N=414) | Sarcopenia (N=511) | P-value |
|---|---|---|---|
| ASM/BMI | |||
| Men (N=927) | |||
| No. of patients | 414 | 511 | |
| Crude model (N=925) | 1.000 (Ref) | 3.569 (2.000–6.369) | <0.0001 |
| Modela (N=925) | - | 2.098 (1.124–3.919) | 0.0202 |
| Modelb (N=789) | - | 2.163 (1.081–4.330) | 0.0294 |
| Modelc (N=758) | - | 1.714 (0.856–3.432) | 0.1276 |
| Women (N=928) | |||
| No. of patients | 455 | 472 | |
| Crude model (N=927) | 1.000 (Ref) | 3.004 (1.808–5.124) | <0.0001 |
| Modela (N=927) | - | 1.805 (1.042–3.128) | 0.0352 |
| Modelb (N=728) | - | 2.130 (1.052–4.312) | 0.0357 |
| Modelc (N=662) | - | 1.898 (0.929–3.878) | 0.0788 |
|
| |||
| (ASM/weight)×100 | |||
| Men (N=927) | |||
| No. of patients | 623 | 303 | |
| Crude model (N=926) | 1.000 (Ref) | 2.790 (1.652–4.713) | 0.0001 |
| Modela (N=926) | - | 1.947 (1.132–3.349) | 0.0162 |
| Modelb (N=790) | - | 2.399 (1.309–4.396) | 0.0048 |
| Modelc (N=759) | - | 1.999 (1.021–3.916) | 0.0434 |
| Women (N=928) | |||
| No. of patients | 607 | 320 | |
| Crude model (N=927) | 1.000 (Ref) | 1.719 (1.039–2.844) | 0.0349 |
| Modela (N=927) | - | 1.337 (0.799–2.236) | 0.2681 |
| Modelb (N=728) | - | 1.181 (0.555–2.513) | 0.6649 |
| Modelc (N=662) | - | 0.922 (0.402–2.114) | 0.8470 |
|
| |||
| ASM/height2 | |||
| Men (N=927) | |||
| No. of patients | 694 | 232 | |
| Crude model (N=926) | 1.000 (Ref) | 1.623 (0.965–2.729) | 0.0677 |
| Modela (N=926) | - | 0.800 (0.439–1.457) | 0.4647 |
| Modelb (N=789) | - | 0.791 (0.363–1.724) | 0.5539 |
| Modelc (N=758) | - | 0.889 (0.414–1.909) | 0.7629 |
| Women (N=928) | |||
| No. of patients | 892 | 36 | |
| Crude model (N=928) | 1.000 (Ref) | 2.705 (0.809–9.050) | 0.1060 |
| Modela (N=928) | - | 2.464 (0.686–8.850) | 0.1662 |
| Modelb (N=728) | - | 1.180 (0.121–11.524) | 0.8866 |
| Modelc (N=663) | - | 1.426 (0.162–12.580) | 0.7488 |
All the analyses were performed using sampling weights. Modela was adjusted for age. Modelb was adjusted for age, waist circumference, smoking status, and regular walker. Modelc was adjusted for age, waist circumference, smoking status, regular walker, hypertension, total cholesterol, triglyceride, and glycated hemoglobin. P-value for interaction (sarcopenia×sex) was calculated from the fully adjusted model for each definition. The P-value were 0.9868, 0.1628, and 0.8346 for appendicular skeletal muscle mass (ASM)/body mass index (BMI), (ASM/weight)×100, and ASM/height2 defined sarcopenia, respectively.
Using the definition of ASM/BMI, a significant association was observed in the crude models for both men (OR, 3.569; 95% CI, 2.000–6.369) and women (OR, 3.004; 95% CI, 1.808–5.124). This association remained significant after adjusting for age, waist circumference, smoking status, and regular walking (Modelb) for both men (OR, 2.163; 95% CI, 1.081–4.330) and women (OR, 2.130; 95% CI, 1.052–4.312). In the fully adjusted model (Modelc), however, the association lost statistical significance for both men (OR, 1.714; 95% CI, 0.856–3.432) and women (OR,1.898; 95% CI, 0.929–3.878).
In terms of (ASM/weight) x 100 definition, the association was significant for men across all models, including the fully adjusted ModelC (OR, 1.999; 95% CI, 1.021–3.916). For women, the association was significant only in the crude model (OR, 1.719; 95% CI, 1.039–2.844), and statistical significance was lost after adjusting for age (Modela) and in subsequent models.
When ASM/height2 definition was used, no statistically significant association was found for men in any model. For women, no significant association was observed, and the confidence intervals were notably wide, which is likely attributable to the small number of participants (N=36).
The test for interaction between sarcopenia status and sex was not statistically significant in the fully adjusted model, P for interaction was 0.9868 for ASM/BMI, 0.1628 for (ASM/weight)×100, and 0.8346 for ASM/height2, respectively.
DISCUSSION
In this nationally representative study of individuals with diabetes, sarcopenia was identified as a factor significantly associated with chronic kidney disease. The results demonstrate that individuals with sarcopenia had 1.826 times the odds of having CKD compared to those without (OR, 1.826; 95% CI, 1.092–3.054). The association between sarcopenia and CKD persisted across multiple adjusted models. This finding suggests that low muscle mass is a health issue closely associated with renal dysfunction in the diabetic population, warranting further longitudinal studies to establish a potential causal relationship.
This finding is consistent with studies detailing the shared pathophysiology between muscle wasting and renal dysfunction. Previous research has established that sarcopenia and CKD share common etiological pathways, including chronic inflammation, hormonal imbalances, and physical inactivity.[25,26] A key proposed mechanism involves the accumulation of uremic toxins as renal function declines. Specifically, protein-bound uremic toxins such as indoxyl sulfate and p-cresyl sulfate have been shown to directly impede muscle function and exacerbate muscle mass loss, a process that worsens as CKD progresses.[ 27,28] These findings provide clinical evidence that aligns with this biological plausibility.
The observed association may be particularly pronounced in the diabetic population because several overlapping pathological processes, originating from chronic hyperglycemia and insulin resistance, converge to damage both muscle and kidney tissue. At the core of this interaction, insulin resistance is not only associated with the development of muscle mass,[29] but also perpetuates the hyperglycemic state that drives further complications. This chronic hyperglycemia, in turn, promotes systemic inflammation and the production of advanced glycation endproducts, leading to vascular complications.[30] This vascular damage appears to be a critical common pathway. Microvascular damage to the glomerulus results in diabetic nephropathy, the primary contributor to chronic renal failure in this population.[31,32] Macrovascular complications, such as peripheral arterial disease (PAD), are also prevalent. PAD impairs blood flow and nutrient delivery to skeletal muscle; a condition independently associated with a higher prevalence of sarcopenia.[33,34] Therefore, these mechanisms are not separated. Insulin resistance and hyperglycemia initiate vascular damage, which simultaneously accelerates renal impairment and muscle degradation, further exacerbating the metabolic dysfunction.
The aging process, a common backdrop for both diabetes and sarcopenia, exacerbates this relationship. Aging is associated with decreased glucose utilization in peripheral tissues and dysregulation of protein turnover – the balance between protein synthesis and breakdown,[35] and its impact is noticeable in diabetes.[36] While insulin is a key promoter of muscle protein synthesis,[37] aging muscles often exhibit a blunted response to its anabolic signals, a concept known as “anabolic resistance”.[38,39] This impaired ability to build muscle protein in response to insulin provides a crucial link in the cycle connecting diabetes, muscle loss, and renal dysfunction.
While the interaction test approached, it did not reach statistical significance by sex. However, the stratified analysis suggested a stronger association between sarcopenia and CKD in men. This trend is consistent with existing literature. For instance, age-related declines in bone and muscle mass tend to coincide more closely in men than in women,[40] and sarcopenia has been more strongly associated with frailty in male hemodialysis recipients.[41]
A biological explanation for the stronger trend in men is the influence of testosterone on muscle homeostasis. Testosterone directly stimulates muscle protein synthesis and growth.[42] Its anabolic effects are mediated through multiple pathways, including the inhibition of myostatin, a negative regulator of muscle growth, and the activation of insulin-like growth factor-1 and mammalian target of rapamycin signaling pathways.[43–45] Crucially, testosterone levels naturally decline with age, a process that has more impact on muscle quality and fat infiltration in men.[46] This decline is exacerbated in renal diseases, with over 60% of men with advanced CKD exhibiting reduced testosterone levels, contributing to muscle wasting.[47]
Furthermore, the association in women is potentially obscured by both methodological challenges in assessing sarcopenia and the subsequent body composition changes in menopausal women. As shown in Table 4, the prevalence of sarcopenia in women was exceptionally low when ASM/ht2 definition was used. The validity of normalizing ASM by height alone has been questioned for Asian female populations due to their relatively greater adiposity and lower lean mass compared to male.[48,49] This methodological underestimation is likely to have resulted in a small sample size, thereby limiting the statistical power to detect an association. This interpretation is supported by the finding that a near-significant trend (OR, 1.898; 95% CI, 0.929–3.878) emerged when using the ASM/BMI definition. It aligns with a growing body of research suggesting that the BMI-adjusted criterion is more predictive of functional decline and disability than height-adjusted definition.[50] Acknowledging this potential, the Asian Working Group for Sarcopenia 2019 consensus encouraged using BMI-adjusted ASM, even as it awaited more evidence before changing formal recommendations.[51]
This study has several limitations. First, its cross-sectional design precludes the establishment of a causal relationship between sarcopenia and CKD. The single-time point measurement for both DM, sarcopenia, and CKD make it impossible to determine the temporal sequence. It was unable to confirm the chronicity of reduced kidney function. Also, CKD definition was based on a single eGFR measurement. Second, with the absence of diabetes duration and long-term glycemic control, the interpretation of analysis is limited. Additionally, there was no data on medication use – such as metformin, which has been suggested to influence muscle mass, or other antidiabetic agents that might indicate disease severity. Finally, potential selection bias may exist. More frail individuals or those with severe and advanced diseases may have been excluded, leading to an underestimation of the association between sarcopenia and CKD in the broader diabetic population.
Despite these limitations, the study also has notable strengths. The use of the KNHANES data ensures a large, nationally representative sample. Furthermore, sarcopenia was robustly defined using a contemporaneous young reference group from the same dataset, following established EWGSOP methodology, which is a more rigorous approach than using arbitrary tertiles.[4] It is differentiated from previous studies using skeletal muscle mass index tertiles. [52,53] Finally, the study’s reliance on objective, directly measured data for key variables, including DXA-based muscle mass and laboratory-based renal function markers, minimizes recall bias and enhances the overall quality and reliability of the finding.
This study demonstrates a significant association between sarcopenia and chronic kidney disease in diabetes using large, nationally representative data. This is one of the first studies to investigate this relationship with a sex-specific sub-group analysis in a large Asian population, highlighting a stronger association observed in men, though it is not statistically significant. Also, it was found that the prevalence of sarcopenia varies by the criteria applied to the diagnosis. The findings establish that low muscle mass is a health indicator linked to renal impairment in diabetic patients. Further prospective research is necessary to confirm these findings and clarify the sex-specific association.
Footnotes
Funding
The authors received no financial support for this article.
Ethics approval and consent to participate
This study conformed to the ethical guidelines of the 1975 Declaration of Helsinki and was approved.
Conflict of interest
No potential conflict of interest relevant to this article was reported.
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