Keywords
HOMA-IR, Insulin Resistance, Sarcopenia, Obesity, Overweight
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Suhardi FL, Aman AM, Sudarso A et al. HOMA-IR Values in Overweight and Obesity Subjects With and Without Sarcopenia [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:313 (https://doi.org/10.12688/f1000research.177884.1)
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Research Article
HOMA-IR Values in Overweight and Obesity Subjects With and Without Sarcopenia
[version 1; peer review: awaiting peer review]
PUBLISHED 24 Feb 2026
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Abstract
Corresponding author:
Feryandi Limanto Suhardi
Competing interests:
No competing interests were disclosed.
Grant information:
The author(s) declared that no grants were involved in supporting this work.
Copyright:
© 2026 Suhardi FL et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Suhardi FL, Aman AM, Sudarso A et al. HOMA-IR Values in Overweight and Obesity Subjects With and Without Sarcopenia [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:313 (https://doi.org/10.12688/f1000research.177884.1)
First published: 24 Feb 2026, 15:313 (https://doi.org/10.12688/f1000research.177884.1)
Latest published: 24 Feb 2026, 15:313 (https://doi.org/10.12688/f1000research.177884.1)
Introduction
Overweight and obesity is recognized as a state of subclinical inflammation characterized by increased infiltration of pro-inflammatory cells, especially macrophages, into adipose tissue. The World Obesity Federation estimates that 800 million people currently live with obesity, with over 1 billion at risk of overweight and obesity. Its impact is evident through associated chronic diseases and reduced productivity.1–3 A key feature of obesity is insulin resistance, where target tissues like skeletal muscle become less responsive to insulin. Adipose tissue, especially visceral fat, secretes inflammatory cytokines and adipokines that impair insulin signaling.4–6
Sarcopenia, the gradual decline of skeletal muscle mass and functionality, is also associated with metabolic imbalance. The Asian Working Group for Sarcopenia (AWGS) 2019 delineates diagnostic criteria that encompass diminished muscle strength, inadequate physical performance, and reduced muscle mass.7 The coexistence of obesity and sarcopenia, termed sarcopenic obesity (SO), presents a significant clinical challenge, associated with higher risks of disability, metabolic syndrome, and mortality.8,9
Insulin resistance is fundamental to the pathophysiology of both disorders. Skeletal muscle serves as the principal location for postprandial glucose use, and insulin is essential for protein synthesis. Resistance to insulin can lead to increased protein catabolism and decreased synthesis, contributing to muscle loss.10,11 Conversely, sarcopenia can induce or exacerbate insulin resistance through mechanisms like abnormal lipid infiltration in muscle and reduced glucose uptake capacity.10,12
The Hyperinsulinemic-Euglycemic Clamp is the gold standard for evaluating insulin resistance; nevertheless, its complexity limits practical application. The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) is a validated, simplified alternative that exhibits a significant correlation with clamp results.13 This study aimed to investigate HOMA-IR values in overweight and obese subjects with and without sarcopenia to explore their interrelationship in an Indonesian clinical setting.
Methods
Study design and setting
This observational study employed a cross-sectional design and was conducted from January 2025 till the sample size was achieved at Dr. Wahidin Sudirohusodo General Hospital and Hasanuddin University Hospital in Makassar, Indonesia.
Study population
The target population was adult patients (>25 years) with overweight or obesity. Participants received verbal and written briefings on data collection methodologies through physical and laboratory examinations. Upon comprehending the sample technique both verbally and in writing, consent was secured through the completion and signing of the informed consent form presented prior to data collection. The accessible population was obese patients visiting the hospitals during the study period. A sample of 100 subjects was determined using a sample size estimation formula for proportion, with a 95% confidence level. Subjects were selected via consecutive sampling.
The eligibility criteria for this study included individuals aged over 25 years, diagnosed with overweight or obesity (BMI ≥23 kg/m2 for overweight, ≥25 kg/m2 for grade 1 obesity, and ≥30 kg/m2 for grade 2 obesity, according to Asian WHO standards), possession of comprehensive medical records encompassing physical examinations, sarcopenia evaluations, and insulin resistance data, and a willingness to participate by providing informed consent. Individuals with anomalies of the central nervous system and problems with balance and coordination were not included.
Operational definitions and measurements
1. Insulin Resistance: Evaluated using HOMA-IR, which is computed as [fasting glucose (mmol/L) x fasting insulin (μU/mL)] /22.5. A cut-off of >2.6 (Tertile 3 of the sample distribution) was used to define insulin resistance.14
2. Sarcopenia: Diagnosed according to AWGS 2019 criteria.
○ Handgrip Strength: Measured using a hand dynamometer. Low strength was defined as <28 kg for men and <18 kg for women.
○ Gait Speed: Measured over a 4-meter walk. Low performance was defined as speed <1.0 m/s.
○ Skeletal Muscle Mass: Assessed using Bioelectrical Impedance Analysis (BIA). Low muscle mass was defined as Appendicular Skeletal Muscle Mass Index (ASMI) <7.0 kg/m2 for men and <5.7 kg/m2 for women.
○ Subjects were classified as “Sarcopenic” if they had low muscle mass plus low strength or low performance. “Presarcopenia” and “Severe Sarcopenia” categories were grouped into “Non-Sarcopenic” and “Sarcopenic,” respectively, for analysis.
3. Overweight/Obesity: Measurements of height and weight were used to determine BMI, and Asian WHO guidelines were used to classify the results.
4. Comorbidities: Data on Type 2 Diabetes Mellitus (T2DM), hypertension, heart disease, and chronic Hepatitis B were collected from medical records.
Statistical analysis
SPSS version 24 was used to analyze the data. Subject characteristics were summarized using descriptive statistics (mean, standard deviation, frequency, and percentage). Spearman correlation analysis was used to examine relationships between HOMA-IR and muscle parameters. Statistical analyses were performed using SPSS version 24, with p < 0.05 considered significant. The relationship between sarcopenia and BMI categories with insulin resistance (HOMA-IR >2.6) was examined using the Chi-square test; a p-value of less than 0.05 was deemed statistically significant.
Results
1. Characteristics of research subjects
A total of 100 participants were included. Baseline characteristics are summarized in Table 1. The cohort had a mean age of 55.87 ± 14.75 years and mean 28.61 ± 5.24 kg/m2. Sarcopenia was identified in 73% of participants, reflecting the hospital-based nature of recruitment. Female participants marginally exceeded male participants (52% compared to 48%). The most common comorbidity was Type 2 Diabetes Mellitus (18%). Based on BMI, 43% had grade 1 obesity. According to HOMA-IR tertiles, 32% of subjects were in Tertile 3 (>2.6). The combination group with both sarcopenia and obesity was the largest (50%).
Table 1. Characteristics of research subjects (n = 100).
2. Demographic characteristics based on insulin resistance
Table 2 presents demographic characteristics stratified by insulin resistance status (HOMA-IR ≤2.6 vs. >2.6). Compared to non-sarcopenic patients (7%), a greater proportion of sarcopenic subjects (25% of the total sample) exhibited HOMA-IR >2.6. Subjects with grade 2 obesity also showed a higher proportion of insulin resistance (14%).
Table 2. Demographic characteristics based on insulin resistance.
3. Mean HOMA-IR values according to combination of sarcopenia and overweight/obesity category
The mean HOMA-IR values for each combination group are shown in Table 3. The highest mean was in the non-sarcopenic obese group (3.08 ± 3.91), and the lowest was in the non-sarcopenic overweight group (1.70 ± 0.26). When compared to the reference group (non-sarcopenic overweight), descriptive differences were noted but no statistically significant changes were discovered.
Table 3. Mean HOMA-IR values according to combination of sarcopenia and overweight/obesity category.
| Combination | n | Mean | SD | p-value* |
|---|---|---|---|---|
| Sarcopenia + Obesity | 50 | 2.67 | 1.09 | 0.442 |
| Sarcopenia + Overweight | 23 | 2.62 | 0.92 | 0.480 |
| Non-Sarcopenia + Obesity | 24 | 3.08 | 3.91 | 0.289 |
| Non-Sarcopenia + Overweight | 3 | 1.70 | 0.26 | (Reference) |
4. Association between combination of sarcopenia and overweight/obesity category with insulin resistance
Table 4 shows the association analysis. The sarcopenic obese group (36.0%) had the descriptively highest prevalence of insulin resistance (HOMA-IR >2.6), followed by the sarcopenic overweight (30.4%) and non-sarcopenic obese (29.2%) groups, while the reference group had 0%. However, the Chi-square test revealed no statistically significant associations between any of the combination groups and insulin resistance (p > 0.05, CI 95%).
Table 4. Association between combination of sarcopenia and overweight/obesity category with insulin resistance.
| Combination of sarcopenia & BMI | Insulin resistance (HOMA-IR >2.6) n (%) | p-value | OR |
|---|---|---|---|
| Sarcopenia + Obesity | 18 (36.0%) | 0.201* | - |
| Sarcopenia + Overweight | 7 (30.4%) | 0.264* | - |
| Non-Sarcopenia + Obesity | 7 (29.2%) | 0.277* | - |
| Non-Sarcopenia + Overweight (Ref.) | 0 (0.0%) | (Reference) |
5. Correlation between insulin resistance and components of sarcopenia
Spearman's correlation analysis ( Table 5) was performed to assess the relationship between the continuous measure of IR (HOMA-IR) and the key quantitative components of sarcopenia. Statistically significant, albeit weak, negative correlations were found between HOMA-IR and both handgrip strength (ρ = -0.225, p = 0.020, 95%) and BIA-measured muscle mass index (ρ = -0.222, p = 0.020, CI 95%). This indicates that as HOMA-IR increases (worsening insulin resistance), there is a tendency for both muscle strength and muscle mass to decrease. In contrast, no statistically significant correlation was observed between HOMA-IR and gait speed (ρ = -0.119, p = 0.238, CI 95%).
Table 5. Correlation between insulin resistance and components of sarcopenia.
| Variables | HOMA-IR ≤2.6 | HOMA-IR >2.6 | Correlation coefficient | P- value |
|---|---|---|---|---|
| Hand Grip Test | -0.225 | 0.020 * | ||
| Male ≥28 kg, Female ≥18 kg | 39 | 15 | ||
| Male <28 kg, Female <18 kg | 29 | 17 | ||
| BIA | -0.222 | 0.020 * | ||
| Male ≥7 kg/m2, Female ≥5.7 kg/m2 | 27 | 9 | ||
| Male <7 kg/m2, Female <5.7 kg/m2 | 41 | 23 | ||
| Gait speed | -0.119 | 0.238* | ||
| ≥1 m/s | 46 | 20 | ||
| <1 m/s | 22 | 12 |
Discussion
In this hospital-based cohort of overweight and obese adults, higher HOMA-IR levels were associated with lower muscle mass and strength, supporting the concept of early metabolic–musculoskeletal interaction. However, sarcopenia status itself was not significantly associated with insulin resistance. These findings contrast with several population-based studies reporting strong associations between sarcopenic obesity and insulin resistance. The unusually high prevalence of sarcopenia observed in our study likely reflects hospital-based sampling and older age distribution rather than population estimates, which may have attenuated between-group comparisons. For example, Lee (2020) and Poggiogalle (2020) found that sarcopenia was linked to increased insulin resistance, particularly when paired with obesity (sarcopenic obesity).15,16 Similarly, Pérez-Cruz (2022) found an odds ratio of 2.224 for insulin resistance in subjects with sarcopenic obesity.17
The discrepancy in findings may be attributed to several factors. First, our study sample had a very high prevalence of sarcopenia (73%), which differs from populations in other studies (e.g., Berriche (2024) reported 7.5%).18 This high baseline prevalence might dilute the comparative effect. Second, the wide age range (27-73 years) and inclusion of various comorbidities could introduce significant confounding that was not fully adjusted for in the analysis. Age itself is a strong independent factor for both sarcopenia and insulin resistance.8,19 Third, methodological differences exist. While we used BIA and AWGS criteria for sarcopenia and HOMA-IR for insulin resistance, other studies used Dual-Energy X-ray Absorptiometry (DXA) or different insulin sensitivity indices like the Matsuda Index, which may yield different results.15
Descriptively, our data showed trends aligning with the pathophysiological link. The mean HOMA-IR was higher in sarcopenic groups compared to non-sarcopenic overweight individuals, and the prevalence of insulin resistance was highest in the sarcopenic obese group. The mechanisms linking these conditions are multifaceted. Insulin resistance can promote sarcopenia by increasing muscle protein breakdown and decreasing synthesis via pathways like FOXO and impaired mTORC1 signaling.10 Conversely, sarcopenia reduces the body's major glucose-disposing tissue, exacerbating hyperglycemia and insulin demand. The inflammatory milieu in obesity, characterized by elevated cytokines like IL-6, can further impair insulin signaling in muscle and promote muscle wasting.10,16
The observed negative correlation (ρ ≈ -0.22) suggests that as IR worsens, its supportive role in maintaining muscle mass and strength is compromised, contributing to the development of sarcopenia. This aligns with studies by AlMuraikhy et al.20 and Kurniawan et al.,21 which also reported inverse relationships between IR and muscle strength metrics. The lack of a significant correlation with gait speed may be attributed to the fact that gait speed is a complex integrative measure of physical function influenced by multiple factors beyond muscle metabolism, including balance, joint health, cardiopulmonary fitness, neurological function, and psychological state.22–24 These confounding variables were not fully accounted for in our analysis.
The lack of statistical significance, despite these descriptive trends, highlights the complexity of the interaction and the influence of unmeasured confounders such as detailed dietary intake (especially protein), physical activity levels, specific medication use, and genetic factors. Furthermore, the use of a single, sample-based tertile cut-off for HOMA-IR (>2.6) rather than a standardized population cut-off may affect comparability.
Study limitations
This study has several limitations. The single-center design and hospital-based sampling limit generalizability to the broader population. The cross-sectional design cannot establish causality. Important confounding variables like precise physical activity, dietary protein intake, and other body composition details were not analyzed. Sarcopenia assessment did not use more advanced tools like DXA. Finally, insulin resistance was assessed only by HOMA-IR, and other indices like the Triglyceride-Glucose (TyG) index were not evaluated.
Conclusion
Among overweight and obese Indonesian adults, higher HOMA-IR levels were correlated with reduced muscle mass and strength. There was no statistically significant association between HOMA-IR values and the combination of sarcopenia and overweight/obesity categories. While descriptive patterns suggested higher insulin resistance in groups with sarcopenia and/or higher BMI. Sarcopenia status was not independently associated with insulin resistance, these findings suggest an early link between metabolic dysfunction and muscle health. Prospective studies with multivariable adjustment and comprehensive body composition assessment are warranted.
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how to cite this article
Suhardi FL, Aman AM, Sudarso A et al. HOMA-IR Values in Overweight and Obesity Subjects With and Without Sarcopenia [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:313 (https://doi.org/10.12688/f1000research.177884.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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