Abstract

Introduction: The bidirectional relationship between sarcopenia and type 2 diabetes mellitus (T2DM) in the elderly creates a detrimental metabolic cycle where insulin resistance accelerates muscle wasting. In resource-limited primary care settings, the lack of advanced diagnostic imaging necessitates the validation of accessible, non-invasive screening tools, such as anthropometric parameters, muscle strength, physical performance, and glycemic control, as surrogate markers for identifying muscle mass depletion.

Aim: To evaluate correlations between anthropometric parameters, muscle strength, physical performance, and glycemic control with skeletal muscle mass (SMM) in elderly patients with T2DM.

Methods: A cross-sectional study was conducted with 203 elderly T2DM patients at a primary healthcare center. Anthropometric measurements included mid-upper arm circumference (MUAC) and calf circumference (CC). The skeletal muscle mass was measured via bioelectrical impedance analysis, muscle strength via handgrip dynamometry, and physical performance via the timed up and go test (TUGT). Glycemic control data (HbA1c and fasting blood glucose) were retrieved from medical records. Data were analyzed using Pearson and Spearman correlation tests and multiple linear regression.

Results: The prevalence of sarcopenia was 41.4%. Multiple linear regression identified CC as the strongest independent predictor of SMM (β=0.370, p=0.000), followed by BMI (β=0.327) and MUAC (β=0.193), with the model explaining 49.6% of SMM variance (R²=0.496).

Conclusion: CC and MUAC are valid, low-cost screening alternatives for SMM assessment in resource-limited settings. CC, in particular, serves as a robust indicator for early sarcopenia detection.

Keywords

calf circumference, diabetes mellitus, mid-upper arm circumference, sarcopenia, skeletal muscle mass

Introduction

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.

Aim

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.

Methods

Subjects

A total of 203 subjects were enrolled in this cross-sectional study, which was conducted at several primary healthcare centers in Semarang, Indonesia. The inclusion criteria comprised patients aged 60 years or older who were diagnosed with T2DM, actively participating in the Chronic Disease Management Program (Prolanis) at the selected health centers, and present at the time of the examination.

The exclusion criteria included patients with a history of conditions (other than T2DM) that could affect skeletal muscle mass, such as thyroid disorders, autoimmune diseases, cancer, Parkinson’s disease, stroke, or a history of hand trauma. These patients were excluded to minimize potential bias in the assessment of muscle mass and strength.

Data for patients with T2DM were obtained from medical records at the primary healthcare centers (Puskesmas) where the study was conducted. Patient participation was confirmed through signed informed consent. FBG and HbA1c values were retrieved as secondary data from laboratory records of patients participating in the Prolanis program.

Anthropometric measurements

Anthropometric measurements included MUAC and CC, which were obtained using a non-elastic measuring tape and calipers. MUAC was measured while the participant was seated in a relaxed position with the arm free of clothing. The midpoint between the acromion and the olecranon process was identified and marked as the measurement site. A non-elastic measuring tape was placed around the arm at this point, ensuring a snug fit without compressing the subcutaneous tissue. Measurements were recorded to the nearest 0.1 centimeter.

CC was measured at the point of maximal circumference on the medial aspect of the calf. Subjects were instructed to stand in a relaxed position, distributing their weight evenly on both legs. The measurement was taken perpendicular to the longitudinal axis of the lower limb using a non-elastic measuring tape. To facilitate accurate identification of the calf’s midline, subjects were permitted to stand on an elevated platform.

The BMI was determined by dividing the subject’s body weight in kilograms by the square of their height in meters. Weight was measured using a calibrated digital scale, and height was measured with a stadiometer, with both parameters recorded while the subjects were wearing light clothing and no shoes.

Muscle strength measurement

Muscle strength was evaluated using a digital Camry Hand Dynamometer (model EH101), which has a maximum capacity of 90 kg and a precision of 0.1 kg. HGS was measured using the dominant hand, following the standardized positioning recommended by the American Society of Hand Therapists (ASHT). During the assessment, participants were seated in a standard position with the shoulder adducted, the elbow flexed at 90°, and the forearm and wrist in a neutral position with the thumb pointing upward. Subjects were instructed to apply maximum squeeze force for 5 seconds. The procedure was performed three times, with a 60-second rest period between trials to prevent muscle fatigue. The highest value from the three attempts was recorded and used for the final analysis.

Physical performance test

Physical performance was evaluated using the TUGT, with results measured using a digital stopwatch. The procedure required participants to sit on a standard chair (approximately 46 cm high), stand up upon a signal, walk a distance of three meters at their usual pace, turn around, return to the chair, and sit down again. Timing commenced the moment the participant’s back left the backrest of the chair and concluded once they were fully seated again. The test was performed twice to ensure consistency, and the best (fastest) time was recorded for the final analysis.

Body composition by BIA

Skeletal muscle mass was measured using a BIA device (Omron Karada Scan HBF-375). Prior to measurement, participant data, including age, sex, and height, were programmed into the device. Participants were instructed to stand upright on the device’s platform without footwear, holding the handgrip electrodes with both hands while extending their arms forward. The device provided the skeletal muscle mass percentage, which was subsequently used to calculate the Skeletal Muscle Index (SMI). Following the Asian Working Group for Sarcopenia (AWGS) and EWGSOP2 criteria, SMI was calculated by adjusting the muscle mass relative to height. Sarcopenia was defined using specific cut-off points for low muscle mass <5.5 kg/m² for women and <7.0 kg/m² for men.

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics (Version 29.0; IBM Corp., Armonk, NY, USA) and GraphPad Prism (Version 10.2.0; GraphPad Software, San Diego, CA, USA). Data normality was assessed using the Kolmogorov–Smirnov test. For normally distributed data (p≥0.05), the Pearson correlation coefficient was employed to evaluate relationships between variables. Conversely, for non-normally distributed data, the Spearman rank correlation test was used. A p-value of ≤0.05 was considered statistically significant. Variables demonstrating significant correlations were subsequently included in a linear regression analysis to determine the predictive strength and the nature of the relationships between the independent and dependent variables.

Results

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).

Table 1.

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Characteristics of subjects (n=203)

Variable	Non-sarcopenia (n=119)	Sarcopenia (n=84)	p-value
Age (years)	66.3±5.6	64.7±5.0	0.058^a
Sex
Male (♂)	32 (15.8%)	23 (11.3%)	0.938^b
Female (♀)	87 (42.9%)	61 (30%)
Body mass index (kg/m²)	22.7±3.3	28.5±4.2	0.000^c*
Underweight (<18.5 kg/m²)	9 (4.4%)	0 (0%)	0.000^b*
Normal (18.5–22.9 kg/m²)	55 (27.1%)	5 (2.5%)
Overweight (23.0–24.9 kg/m²)	21 (10.3%)	10 (4.9%)
Obesity Class 1 (25.0–29.9 kg/m²)	33 (16.3%)	43 (21.2%)
Obesity Class 2 (≥30 kg/m²)	1 (0.1%)	26 (12.8%)
Fasting blood glucose (mg/dL)	151.9±54.1	152.3±65.1	0.464^a
Controlled (≤130 mg/dL)	50 (24.6%)	42 (20.7%)	0.260^b
Poor control (>130 mg/dL)	69 (34%)	42 (20.7%)
HbA1C (%)	8.2±1.9	8.2±2.1	0.851^c
Controlled (<7%)	28 (13.8%)	23 (11.3%)	0.616^b
Inadequate control (7-8%)	10 (4.9%)	10 (4.9%)
Poor control (>8%)	41 (20.2%)	26 (12.8%)
Mid-upper arm circumference (cm)	26.3±2.8	29.8±3.2	0.000^c*
Normal (♂<28.6 cm; ♀<27.5 cm)	34 (16.7%)	59 (29.1%)	0.000^b*
Poor (♂≥28.6 cm; ♀≥27.5 cm)	86 (42.4%)	24 (11.8%)
Calf circumference (cm)	30.7±2.7	35.5±3.4	0.000^c*
Normal (♂<34 cm; ♀<33 cm)	20 (9.9%)	71 (35%)	0.000^b*
Poor (♂≥34 cm; ♀≥33 cm)	100 (49.3%)	12 (5.9%)
Hand grip strength (kg)	18.2±5.6	17.5±5.4	0.377^c
Normal (♂<28 kg; ♀<18 kg)	38 (18.7%)	24 (11.8%)	0.609^b
Poor (♂≥28 kg; ♀≥18 kg)	81 (39.9%)	60 (29.6%)
Timed up and go test (s)	12.7±3.5	13.7±4.3	0.054^a
Normal (<14 s)	92 (45.3%)	50 (24.6%)	0.006^b*
High fall risk (≥14 s)	27 (13.3%)	34 (16.7%)

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).

Table 2.

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Correlation analysis of anthropometric and clinical parameters with skeletal muscle mass and glycemic control

Variable pair	Correlation coefficient (r)	p-value	Coefficient of determination (R²)
HbA1c and SMM	−0.028	0.748^a	0.000
BMI and SMM	0.655	0.000^a*	0.429
CC and SMM	0.705	0.000^b*	0.497
MUAC and SMM	0.598	0.000^b*	0.357
HGS and SMM	0.003	0.963^a	0.000
TUGT and SMM	0.102	0.148^a	0.010
BMI and HbA1c	0.044	0.618^a	0.002
CC and HbA1c	−0.006	0.941^a	0.000
MUAC and HbA1c	0.075	0.380^a	0.006
HGS and HbA1c	0.166	0.051^a	0.028
TUGT and HbA1c	−0.075	0.381^a	0.006

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.

Figure 1.

Linear regression analysis graph between A) skeletal muscle mass (SMM) and calf circumference (CC); and B) skeletal muscle mass (SMM) and mid-upper arm circumference (MUAC)

Table 3.

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Multiple linear regression analysis for predicting skeletal muscle mass (SMM)

Variable	B	SE	Standardized Beta (β)	p-value
(Constant)	−1.745	0.510	-	0.001
Calf circumference	0.115	0.020	0.370	0.000*
Mid-upper arm circumference	0.066	0.020	0.193	0.001*
Body mass index	0.083	0.016	0.327	0.000*

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)

Discussion

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.

Conclusion

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.

Acknowledgements

We would like to express our sincere gratitude to the Research and Community Service Institute of Universitas Muhammadiyah Semarang, Indonesia, for providing funding through the UNIMUS Internal Grant, as stated in the Agreement Letter No. 014/UNIMUS.L/PG/PDP/PJ.INT/2025.

References

1. Indonesian Endocrinology Association. Management and prevention of type 2 diabetes mellitus in Indonesia 2021. Jakarta: PB PERKENI; 2021. Available from: https://pbperkeni.or.id/wp-content/uploads/2021/11/22-10-21-Website-Pedoman-Pengelolaan-dan-Pencegahan-DMT2-Ebook.pdf

2. Milita F, Handayani S, Setiaji B. Incidence of type II diabetes mellitus in the elderly in Indonesia (2018 Riskesdas Analysis). J Kedokt Kesehat 2021; 17(9):116–23. doi: 10.24853/jkk.17.1.9-20

3. Rosma A, Gunawan D, Paskaria C. The effect of type 2 diabetes mellitus on sarcopenia in elderly. J Med Health 2022; 4:145–53. doi: 10.28932/jmh.v4i2.2886

4. Magliano DJ, Boyko EJ; IDF Diabetes Atlas 10th edition scientific committee. IDF DIABETES ATLAS [Internet]. 10th edition. Brussels: International Diabetes Federation; 2021. Available from: https://www.ncbi.nlm.nih.gov/books/NBK581934

5. Berlian M, Desiani E. Evaluation of insulin type suitability and dosage accuracy for type 2 diabetes mellitus outpatients at Kajen Regional General Hospital, January - June 2023. J Curr Pharm Sci 2024; 7(2):725. doi: 10.35747/jcps.v8i1.1539

6. Djatmiko M. Health report of the Semarang City Health Office. Semarang City Health Office. 2022;6(1):1–6. Available from: https://profil-kesehatan.dinkes.semarangkota.go.id/upload/publication/Profil%20Kesehatan%202022.pdf

7. Pusparini ND, Probosari E, Murbawani EA, et al. Diagnostic accuracy of calf circumference for decreased muscle mass in older adults with sarcopenia. J Biomed Transl Res 2022; 1(1):1–6. doi: 10.14710/jbtr.v1i1.12115

8. Scheerman K, Meskers CGM, Verlaan S, et al. Sarcopenia, low handgrip strength, and low absolute muscle mass predict long-term mortality in older hospitalized patients: an observational inception cohort study. J Am Med Dir Assoc 2021; 22(4):816–20. doi: 10.1016/j.jamda.2020.12.016

9. Martinez BP, Gomes IB, de Oliveira CS, et al. Accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients. Clinics (Sao Paulo) 2015; 70(5):369–72. doi: 10.6061/clinics/2015(05)11

10. Xu Y, Hu T, Shen Y, et al. Association of skeletal muscle mass and its change with diabetes occurrence: a population-based cohort study. Diabetol Metab Syndr 2023; 15(1):1–8. doi: 10.1186/s13098-023-01027-8

11. Hajar N, Tatius B, Basuki R, et al. Calf circumference is associated with cardiorespiratory fitness in medical students: a cross-sectional study. In: Proceedings of the XV Congress & 52nd Anniversary of PAAI 2023 – 4th LUMMENS: The Role of Gut Brain Axis in Indonesian Human Development. 2023. p. 259–64. Available from: https://fk.ulm.ac.id/ojs/index.php/lummens/article/download/203/177

12. Ren S, Huang S, Chen M, et al. Association between the mid-upper arm circumference (MUAC) and calf circumference (CC) screening indicators of sarcopenia with the risk of pneumonia in stable patients diagnosed with schizophrenia. Front Psychiatry 2022; 13:931933. doi: 10.3389/fpsyt.2022.931933

13. Ogama N, Sakurai T, Kawashima S, et al. Association of glucose fluctuations with sarcopenia in older adults with type 2 diabetes mellitus. J Clin Med 2019; 8(3):319. doi: 10.3390/jcm8030319

14. Hu FJ, Liu H, Liu XL, et al. Mid-upper arm circumference as an alternative screening instrument to appendicular skeletal muscle mass index for diagnosing sarcopenia. Clin Interv Aging 2021; 16:1095–104. doi: 10.2147/CIA.S311081

15. Gamayanti KAV, Aryana IGPS, Gotera W. The relationship between handgrip strength and random blood sugar levels in the elderly in Melinggih Village, Payangan District, Gianyar Regency. Intisari Sains Medis 2023; 14(2):483–8. doi: 10.15562/ism.v14i2.1716

16. Queiroz LL, da Silva LGO, Pinheiro HA. Can the timed up and go test be used as a predictor of muscle strength in older adults? Fisioter Pesqui 2023; 30:e22013723. doi: 10.1590/1809-2950/e22013723en

17. Xu Y, Hu T, Shen Y, et al. Association of skeletal muscle mass and its change with diabetes occurrence: a population-based cohort study. Diabetol Metab Syndr 2023; 15:53. doi: 10.1186/s13098-023-01027-8

18. Chen LK, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc 2020; 21(3):300–7.e2. doi: 10.1016/j.jamda.2019.12.012

19. Kim S, Kim M, Lee Y, et al. Calf circumference as a simple screening marker for diagnosing sarcopenia in older Korean adults: The Korean Frailty and Aging Cohort Study (KFACS). J Korean Med Sci 2018; 33(20):e151.

20. Yasuda T. Simplified morphological evaluation of skeletal muscle mass and maximum muscle strength in healthy young women: Comparison between thigh and calf. Womens Health (Lond) 2020; 16:1745506520962009. doi: 10.3346/jkms.2018.33.e151

21. Cavedon V, Milanese C, Zancanaro C. Are body circumferences able to predict strength, muscle mass and bone characteristics in obesity? A preliminary study in women. Int J Med Sci 2020; 17(7):881–91. doi: 10.7150/ijms.41713

22. Rahayu SR, Raharjo TJ, Nugroho H, et al. Sarcopenia in indonesian community-dwelling elderly: the prevalence and influencing factors within the primary care setting. Unnes J Public Health 2025; 14(1):32–45. doi: 10.15294/ujph.v14i1.16058

23. Hou L, Xiang L, Wang S, et al. Pathogenesis and comprehensive treatment strategies of sarcopenia in elderly patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne) 2024; 14:1263650. doi: 10.3389/fendo.2023.1263650\

24. Hamasaki H. Obesity paradox in individuals with type 1 diabetes. Front Clin Diabetes Healthc 2025; 6:1670312. doi: 10.3389/fcdhc.2025.1670312

25. Kiss CM, Bertschi D, Beerli N, et al. Calf circumference as a surrogate indicator for detecting low muscle mass in hospitalized geriatric patients. Aging Clin Exp Res 2024; 36(1):25. doi: 10.1007/s40520-024-02694-x

26. Mori H, Kuroda A, Ishizu M, et al. Association of accumulated advanced glycation end-products with a high prevalence of sarcopenia and dynapenia in patients with type 2 diabetes. J Diabetes Investig 2019; 10(5):1332–40. doi: 10.1111/jdi.13014

27. Sugimoto K, Tabara Y, Ikegami H, et al. Hyperglycemia in non-obese patients with type 2 diabetes is associated with low muscle mass: The Multicenter Study for Clarifying Evidence for Sarcopenia in Patients with Diabetes Mellitus. J Diabetes Investig 2019; 10(6):1471–9. doi: 10.1111/jdi.13070

28. Dwipa L, Apandi M, Utomo PP, et al. Adaptation and validation of the Indonesian version of the FRAIL scale and the SARC-F in older adults. Asian J Gerontol Geriatr 2021; 16(1):40-46. doi: 10.12809/ajgg-2020-436-oa

Additional information

Ethical statement

The authors declared that no clinical trials were used in the present study.
The authors declared that no experiments on humans or human tissues were performed for the present study.
The authors declared that written informed consent to participate in this study was provided by the participants of the study.
The study protocol was approved by the Ethics Committee of the Faculty of Medicine, Universitas Muhammadiyah Semarang (No. 003/EC/KEPK-FK/UNIMUS/2025).
The authors declared that no experiments on animals were performed for the present study.
The authors declared that no commercially available immortalized human and animal cell lines were used in the present study.

Conflict of interest

The authors have declared that no competing interests exist.

Artificial Intelligence (AI) use

The authors accept full responsibility for the content of the manuscript, including the disclosure of any use of AI. No AI tools were used in the preparation of this manuscript.

Funding

This research was funded by the UNIMUS Internal Grant from the Research and Community Service Institute (LPPM) of Universitas Muhammadiyah Semarang, Indonesia.

Author contributions

AAZ led the study conceptualization, grant acquisition, data analysis, and primary manuscript drafting. NH and LF managed the study design, ethical clearance, and grant procurement. GNA provided expert clinical oversight and performed critical revisions of the manuscript. AAZ, NH, LF, MS, BIA, HGS, TUGT, and TA executed the anthropometric measurements and medical record extraction. TA, MZ, TIM, and BIP facilitated data collection across the primary health centers and technical processing. All authors approved the final version for submission.

Author ORCIDs

Alif Adlan Zulizar https://orcid.org/0009-0005-3899-8691

Nabil Hajar https://orcid.org/0000-0003-0830-5856

Lukman Faishal Fatharani https://orcid.org/0009-0007-2816-3564

Gita Nurtaningtyas Aini https://orcid.org/0009-0000-4967-3415

Tsaqifatul A’izza https://orcid.org/0009-0003-1563-6220

Mickhael Sandyka https://orcid.org/0009-0003-8399-5399

Muhamad Zidan Akmal Hisani https://orcid.org/0009-0009-8907-1991

Bagas Iqbal Pahlevi https://orcid.org/0009-0005-2652-1650

Thoha Ibnu Muwaffaq https://orcid.org/0009-0002-6878-2187

Data availability

All of the data that support the findings of this study are available in the main text.