Type 2 diabetes mellitus (T2DM) is a progressive metabolic disorder characterized by chronic hyperglycemia resulting from insulin resistance, impaired insulin secretion, or both.‌^[1] Among the elderly, it is frequently accompanied by geriatric syndromes such as sarcopenia, a condition defined by the age-related loss of skeletal muscle mass and strength.‌^[2] This dual burden contributes to diminished physical capacity, reduced quality of life, and increased morbidity and mortality.^[3]

The global and national prevalence of T2DM continues to rise, particularly within the geriatric population. According to the International Diabetes Federation (IDF), an estimated 589 million people worldwide will be living with diabetes by 2025, representing a prevalence of 11.1% among adults aged 20–79 years.^[4] Indonesia currently ranks fifth globally with 19.5 million cases, a figure projected to reach 28.6 million by 2045.^[5] In Central Java, 647,093 cases of diabetes were reported in 2022, 90% of which were classified as T2DM. Specifically, in Semarang, 41,468 cases of T2DM were recorded in 2023.^[6]

T2DM with complications is the third leading cause of death in Indonesia, with a mortality rate of 6.7%.^[2] A significant contributor to this mortality is sarcopenia, a syndrome characterized by the progressive physiological decline of muscle mass and function. According to the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGS), a diagnosis of sarcopenia is based on three primary criteria: reduced muscle mass, decreased muscle strength, and impaired physical performance.^[7] In Indonesia, the general prevalence of sarcopenia is approximately 40.6%; however, among elderly individuals with T2DM, the prevalence is reported to be 15.7%. The coexistence of T2DM and sarcopenia exacerbates the risk of mortality and significantly impairs functional independence.^[2]

Despite its clinica l significance, sarcopenia often remains undiagnosed due to limited access to gold-standard diagnostic tools, such as bioelectrical impedance analysis (BIA), handgrip dynamometry, and the Timed Up and Go Test (TUGT).^[8,9] Consequently, simpler anthropometric alternatives, such as mid-upper arm circumference (MUAC) and calf circumference (CC), have emerged as practical screening options for primary healthcare settings, particularly in resource-limited areas.^[10-12]

Furthermore, poor glycemic control, indicated by elevated HbA1c and fasting blood glucose (FBG), is known to accelerate muscle catabolism and diminish physical function in the elderly.^[13] Nevertheless, studies assessing the predictive value of MUAC, CC, and glycemic status for sarcopenia specifically among elderly T2DM patients remain scarce, particularly in Semarang. Therefore, this study is essential to facilitate early detection and implement preventive strategies for this high-risk population.

Previous research has demonstrated that MUAC and CC provide comparable accuracy to BIA in assessing muscle mass among healthy older adults. Specifically, CC is positively correlated with skeletal muscle mass, while handgrip strength has been inversely linked to glycemic levels; higher blood glucose is significantly associated with reduced grip strength.^[7,14,15] While the TUGT shows a moderate correlation with lower-limb strength, its correlation with handgrip strength remains weak.^[16] Although reduced skeletal muscle mass is a known risk factor for the development of diabetes, research focusing on sarcopenia within the T2DM population remains limited.^[17] Despite this elevated risk, research addressing sarcopenia specifically among individuals with T2DM remains limited. Accordingly, the present study evaluates older adults with T2DM by utilizing practical anthropometric parameters, muscle strength, and physical performance to predict muscle mass, while also examining glycemic control as a screening component.

The objective of this study was to evaluate the correlations between anthropometric measurements, muscle strength, and physical performance indicators with skeletal muscle mass and glycemic status among elderly individuals with type 2 diabetes mellitus.

In this study, demographic and clinical characteristics were compared between the non-sarcopenia (♂<7 kg/m²; ♀<5.7 kg/m²) and sarcopenia (♂≥7 kg/m²; ♀≥5.7 kg/m²) groups to identify key physiological differences in elderly patients with T2DM. Statistical analysis revealed no significant differences between groups regarding age, sex distribution, or glycemic status (fasting blood glucose and HbA1c levels). However, BMI was significantly higher in the sarcopenia group (28.5±4.2 kg/m² vs. 22.7±3.3 kg/m²; p=0.000), reflecting a higher prevalence of obesity compared to the non-sarcopenia group. This trend extended to anthropometric measurements, where the sarcopenia group exhibited significantly larger mean MUAC (29.8±3.2 cm vs. 26.3±2.8 cm; p=0.000) and calf circumference (35.5±3.4 cm vs. 30.7±2.7 cm; p=0.000). Conversely, functional performance did not differ significantly, as demonstrated by the lack of statistical difference in HGS (p=0.377) and TUGT times (p=0.054) (Table 1).

Correlation analysis identified strong positive associations between anthropometric parameters and SMM among elderly patients with T2DM. Calf circumference demonstrated the highest correlation with SMM (r=0.705; p=0.000), followed by body mass index (r=0.655; p=0.000) and MUAC (r=0.598; p=0.000). These results indicate a positive direction of correlation, where an increase in anthropometric measurements is associated with an increase in SMM. The coefficients of determination (R²) further illustrate the predictive power of these variables, with CC explaining approximately 49.7% of the variance in SMM, while BMI and MUAC explain 42.9% and 35.7%, respectively (Table 2).

The linear regression graphs in Fig. 1 visually confirm these strong relationships, showing a clear upward trend in the data points for both CC and MUAC against SMM. In Fig. 1A, the regression line highlights the robust linear dependency between CC and SMM, reinforcing CC as a reliable predictor of muscle mass in this population. Similarly, Fig. 1B displays a consistent positive slope for MUAC, though the slightly wider scatter of data points reflects its comparatively lower correlation coefficient and R² value compared to CC.

A multiple linear regression analysis was conducted to evaluate the predictive capacity of anthropometric measurements on SMM among elderly patients with type 2 diabetes. The results indicated that the overall model significantly predicted SMM (p<0.05) demonstrating that clinical measurements can effectively estimate muscle volume when advanced imaging is unavailable. Among the independent variables, CC emerged as the most robust predictor, yielding the highest standardized coefficient (β=0.370, p=0.000) , which underscores its clinical value as a primary proxy for muscle mass in geriatric screening. This was followed by BMI (β=0.327, p=0.000) and MUAC (β=0.193, p=0.001), both of which contributed significantly to the model’s accuracy.

Based on these findings, the derived regression equation for estimating SMM in this population is:

SMM=−1.745 + (0.115×CC) + (0.066×MUAC) + (0.083×BMI)

This study highlights CC as the most robust independent predictor of SMM in geriatric patients with T2DM. Multiple linear regression results (R²=0.496) demonstrate that CC explains nearly half of the SMM variance, affirming its role as a superior screening surrogate compared to MUAC.‌^[18,19] The anatomical distribution of skeletal muscle in the lower extremities makes CC particularly sensitive to age-related atrophy. Our findings indicate that every 1 cm increase in CC corresponds to a 0.115 kg/m² increase in SMM, providing clinicians with a quantifiable monitoring tool.^[20]

While MUAC served as a significant predictor, its explanatory power (R²=0.360) was limited. This discrepancy is likely due to the “fat-masking” effect often observed in T2DM, where subcutaneous adiposity in the upper arms can inflate measurements and obscure underlying muscle loss.^[21] Conversely, CC is less confounded by localized fat distribution in Asian elderly populations, making it a more reliable proxy for lean tissue mass.^[18]

The observed sarcopenia prevalence of 41.4% reflects a substantial health burden in this cohort. This figure is significantly higher than rates previously reported in community-dwelling Indonesian elderly, which typically range from 44% to 50%.^[22] The elevated prevalence in our study highlights the accelerated muscle wasting characteristic of diabetic populations, where metabolic stress compounds the natural aging process.^[23]

Our data support the “obesity paradox,” as the non-sarcopenic group exhibited significantly higher BMI and a greater frequency of Class 2 obesity. This suggests that a low or “normal” weight in elderly diabetics may be a clinical “red flag” for muscle wasting, whereas a higher BMI may serve as a protective nutritional and metabolic buffer.‌^[24] Consequently, clinicians should prioritize body composition over weight alone when assessing geriatric risk.^[25]

The lack of significant correlation between glycemic control indicators (HbA1c) and sarcopenia status suggests a temporal decoupling. While blood glucose reflects medium-term metabolic fluctuations, skeletal muscle loss is a cumulative result of long-term metabolic dysregulation.^[26] This implies that a single point of stable HbA1c may mask underlying structural muscle decline.^[27]

Pathophysiologically, the synergy between aging and chronic hyperglycemia in T2DM promotes the accumulation of advanced glycation end products (AGEs) and systemic inflammation. These factors activate the ubiquitin-proteasome pathway and inhibit insulin-like growth factor-1 (IGF-1) signaling, creating an imbalance where protein breakdown outweighs synthesis.^[23] This metabolic environment makes the diabetic muscle highly prone to rapid functional failure.^[26]

These findings have significant implications for Indonesian primary healthcare (Puskesmas). The validation of CC and MUAC as reliable, low-cost screening tools allows for the implementation of sarcopenia assessment in settings where expensive technologies like DXA are unavailable. ^[25] Early identification at the Puskesmas level is crucial for initiating nutritional and physical interventions before permanent disability occurs.^[22]

Integrating the Indonesian version of screening tools, such as the Ina-SARC-F, alongside CC measurements, can further enhance diagnostic accuracy in resource-limited settings.^[28] Our results help define specific categories of “poor” limb circumferences that can serve as a simple triage system during routine geriatric health visits.^[22] This practical framework is highly compatible with the heavy workload of Indonesian primary care professionals.^[28]

This study is limited by its cross-sectional design and localized sample in Semarang, which precludes definitive causal conclusions and limits broad generalizability. Future multi-center longitudinal research is necessary to refine Indonesian-specific cutoff values and monitor the long-term impact of sarcopenia management. Despite these limitations, this research provides a scientific basis for adopting anthropometric markers as a vital component of geriatric diabetes care in Indonesia.

Calf circumference and mid-upper arm circumference are significant predictors of skeletal muscle mass in T2DM patients, with CC being the most robust marker accounting for nearly 50% of the variance in muscle mass. These findings validate CC and MUAC as effective, low-cost screening alternatives for sarcopenia in resource-limited primary healthcare settings. Primary healthcare providers should integrate CC measurements into routine monthly evaluations for T2DM patients to ensure early detection of muscle wasting. Future research should focus on establishing population-specific cut-off values to optimize the diagnostic accuracy of these markers in Indonesia.