Role of muscle glucometabolism derived from ¹⁸F-FDG PET/CT in sarcopenia of elderly patients with newly diagnosed malignancies

Introduction

Currently, with constant improvement in health care and living quality, approximately 22% of the world population will be over 60 years by 2050 (1), and this elderly population are the predominant contributors to the high incidence (60%) and mortality rate (70%) of cancers (2). Among the common comorbidities in this population, sarcopenia has gained increasing research interest, as it not only reduces the quality of life but also increases the risk of toxic side effects of anticancer treatments (3,4).

Sarcopenia, defined as an age-associated involuntary loss of skeletal muscle mass accompanied by a decline in muscle function, usually appears as an overall decrease in the size and number of skeletal muscle fibers and marked infiltration of fibrous and adipose tissues into the skeletal muscle (5,6). At present, imaging modalities have been widely utilized to measure such changes in skeletal muscles. Specifically, skeletal muscle index (SMI), measured as skeletal muscle area (SMA) at the 3^rd lumbar (L3) level and normalized to the height of the patient, by computed tomography (CT) imaging, is the most accepted diagnostic method (7). However, it is cumbersome and inconvenient for clinicians as it requires dedicated software and additional manual or semi-automatic measurement, which is currently not included in most commercially available imaging workstations (8).

Nowadays, ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT has emerged as the de facto imaging modality of choice in oncology practice due to its superior diagnostic performance and increased clinical availability. As a result, the measurement of SMI based on the CT component of PET/CT images has become practical (9). On the other hand, ¹⁸F-FDG PET/CT images offer semi-quantitative parameters such as maximum standardized uptake value (SUVmax) to probe the glucometabolic activity across the whole body (10). Our previous studies have found that certain cancer patients with sarcopenia tend to present with lower glucometabolism within skeletal muscles (11,12). Hence, we hypothesize that the SUVmax of skeletal muscle might be decreased in all cancer patients with sarcopenia and could serve as a diagnostic factor for sarcopenia. Thus, this study measured the SUVmax of psoas major muscle in 1,024 elderly cancer patients with or without sarcopenia to assess the association between sarcopenia, PET/CT-derived parameters, and clinicopathological factors. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-705/rc).

Methods

Study cohort

From November 2021 to September 2023, the consecutive patients with newly diagnosed malignancies who underwent pre-treatment ¹⁸F-FDG PET/CT scans at Guangdong Provincial People’s Hospital were retrospectively analyzed in this study. A total of 1,024 patients [638 men, 386 women; median age: 68 years (range, 60–99 years)] were ultimately enrolled based on the following criteria: (I) age ≥60 years; (II) newly discovered malignant tumor without prior anti-tumor therapy; (III) without renal and hepatic impairment; (IV) no history of muscle nutritional or metabolic disease; (V) fasting glucose <11.0 mmol/L after management; (VI) no abnormal psoas muscle morphology or FDG uptake secondary to exercise, injury, diseases, or iatrogenic causes; (VII) adequate image quality; and (VIII) complete clinical information (Figure 1). This study was approved by the ethics committee of Guangdong Provincial People’s Hospital (No. KY2024-534-01) and was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consent was waived due to the retrospective nature of the study.

Figure 1 Patient flowchart showing the enrolled criteria. FDG, fluorodeoxyglucose.

Clinicopathological data were collected including age, sex, body mass index (BMI), smoking history, chronic obstructive pulmonary disease (COPD), cardiovascular disease (including chronic heart failure, decompensated heart failure, coronary artery disease, arrhythmias and congenital heart disease), diabetes status, Eastern Cooperative Oncology Group (ECOG) performance status (PS), primary tumor location and number of each patient. Patients were categorized into four groups based on their BMI values: underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥30.0 kg/m²).

18F-FDG PET/CT scan and patient management

Before imaging, all patients fasted for ≥6 h and avoided strenuous exercise for ≥24 h. To simplify the patient preparation protocol, we strictly followed the EANM guidelines for the management of diabetic patients, including cessation of oral antidiabetic drugs, proper patient arrangement, and temporal use of rapid-acting insulin ≥6 h before tracer injection to ensure blood glucose <11.0 mmol/L (13).

¹⁸F-FDG PET/CT scan was performed using a total-body PET/CT scanner (uEXPLORER, UIH, Shanghai, China) with an axial field of view of 194 cm. Images were required approximately 60±5 min after intravenous injection of ¹⁸F-FDG at a dose of 3.7 MBq/kg. Low-dose CT scans (120 kV, 100 mA) were reconstructed in a 512×512 matrix for attenuation correction. Subsequently, total-body PET imaging was performed using a 5-min list-mode acquisition. All PET images were reconstructed using a three-dimensional (3D) ordered-subset expectation maximization algorithm. A Gaussian filter with a full-width half-maximum of 3 mm was applied to the reconstructed images.

PET/CT imaging analysis

PET/CT images were analyzed by two nuclear medicine physicians who were blinded to the clinical information of the patients and worked independently of each other, using the uWS-MI station (UIH). According to the previous study (12,14), volume of interest (VOI) of skeletal muscle with a diameter of 1.0 cm was manually drawn on the bilateral psoas muscle at the L3 level (Figure S1A). The maximum value within each VOI was recorded as SUVmax. Muscle SUVmax was calculated as the average of three repeated VOI measurements of the psoas muscle on each side. The SUVmax measurement of the psoas muscle at the L3 level was selected because the thick bundle of the muscle fibers was easy to locate and delineate and usually free of interference from other organs.

SMI was measured based on the CT portion of PET/CT using ImageJ software (version 2.9.0). Hounsfield units (HU) were used to identify skeletal muscle with the threshold ranging from -29 to 150 HU (8). Subsequently, SMA (cm²) (Figure S1B) was calculated at the L3 level and normalized for the height in square meters and expressed as SMI (cm²/m²). The cut-off value of the SMI used to identify sarcopenia was defined as <44.77 cm²/m² in males and <32.50 cm²/m² in females referred to the previous study (15).

Statistical analysis

SPSS (version 26.0, IBM Corp., Armonk, NY, USA) or R (version 4.3.3, R Foundation for Statistical Computing, Vienna, Austria) was utilized for data management and analysis. Continuous variables were expressed as medians (ranges), while categorical variables were presented as frequencies. Comparison of continuous variables was conducted using the independent samples t-test or Mann-Whitney U test, and categorical variables were compared using Chi-squared test (χ² test) or Fisher’s exact test. A multivariable logistic regression following the univariate logistic regression was conducted to identify factors predicting sarcopenia using a forward stepwise approach. Based on Harrell’s guidelines (16), a nomogram model was established to predict sarcopenia by only inputting independent factors. The total score obtained from the nomogram model was used to calculate the sarcopenia score. The optimal threshold for predicting sarcopenia was determined using the receiver operating characteristic (ROC) curve. Specifically, 10-fold internal cross-validation was adopted to adjust the aforementioned models and to minimize overfitting. A P value of <0.05 was considered statistically significant.

Results

Patients’ demographics

The enrolled 1,024 patients consisted of 924 patients with a single primary tumor, 92 patients with dual primary tumors, and 8 patients with triple primary tumors. Their scan indication and final diagnosis were summarized in Table 1, including lung cancer (n=628, 55.5%), breast cancer (n=89, 7.9%), digestive system tumors (n=215, 19.0%), urological system tumors (n=82, 7.2%), head and neck tumors (n=61, 5.4%), gynecological tumors (n=40, 3.5%), hematological system tumors (n=14, 1.2%), and tumors of other origins (n=3, 0.3%).

Among these 1,024 patients, 44.8% of patients (459/1,024) were diagnosed with sarcopenia, while 55.2% of patients (565/1,024) were non-sarcopenic. The median BMI of these 1,024 patients was 22.3 kg/m² with a range from 13.0 to 33.8 kg/m², and 107 patients (10.4%) were underweight, 704 patients (68.8%) were normal weight, 195 patients (19.0%) were overweight, and 18 patients (1.8%) were obese. Moreover, 360 patients (35.2%) had smoking history, 19 patients (1.9%) had COPD, 116 patients (11.3%) had cardiovascular diseases, 179 patients (17.5%) had diabetes, and 645 patients (63.0%) presented with poor ECOG status (PS of 2–4). The clinical characteristics of the patients are listed in Table 2.

Factors associated with sarcopenia

As shown in Table 2, sarcopenia was more common in men, patients with smoking history, diabetic status, low BMI, poor ECOG status, and multiple primary tumors (P<0.05). Additionally, SUVmax of muscle in sarcopenic patients was significantly lower than that in non-sarcopenic patients (P<0.05, Figure 2). No significant difference was observed in age and fasting glucose level (P>0.05). Based on the ROC analysis, the cross-validation adjusted best cut-off value of SUVmax of muscle for distinguishing sarcopenia from non-sarcopenia was 0.875, the area under the curve was 0.608 [P<0.001, 95% confidence interval (CI): 0.573–0.642], and the sensitivity and specificity were 52.9% and 64.3%, respectively (Figure S2).

Figure 2 Difference in SUVmax of muscle between sarcopenic and non-sarcopenic patients. SUVmax, maximum standardized uptake value.

Univariate logistic regression identified that sex [odds ratio (OR) =1.697; 95% CI: 1.310–2.198; P<0.001], BMI (OR =0.952; 95% CI: 0.916–0.990; P=0.013), number of primary tumors (OR =2.067; 95% CI: 1.355–3.154; P=0.001), smoking history (OR =1.429; 95% CI: 1.104–1.849; P=0.007), diabetes (OR =1.415; 95% CI: 1.024–1.955; P=0.035), ECOG status (OR =1.670; 95% CI: 1.289–2.165; P<0.001), and SUVmax of muscle (OR =0.151; 95% CI: 0.078–0.291; P<0.001) were significantly associated with sarcopenia (Table 3). Further multivariate logistic regression revealed that sex (OR =1.811; 95% CI: 1.383–2.371; P<0.001), BMI (OR =0.956; 95% CI: 0.918–0.995; P=0.028), number of primary tumors (OR =2.155; 95% CI: 1.387–3.348; P=0.001), ECOG status (OR =1.909; 95% CI: 1.453–2.509; P<0.001), and SUVmax of muscle (OR =0.110; 95% CI: 0.055–0.220; P<0.001) independently predicted sarcopenia (Table 3). Subsequently, a nomogram model for predicting the probability of sarcopenia was developed by using these independent variables (Figure 3).

Figure 3 A nomogram model to predict the probability of sarcopenia. BMI, body mass index; ECOG, Eastern Cooperative Oncology Group; SUVmax, maximum standardized uptake value.

Factors associated with sarcopenia stratified by sex

As the diagnostic criteria for sarcopenia differ between males and females, we also examined factors associated with sarcopenia stratified by sex (Table 4). Regardless of sex, the SUVmax of muscle was lower in sarcopenic patients compared to non-sarcopenic patients (P<0.05). In addition, sarcopenia was more likely to occur in male patients with lower BMI, poor ECOG status, and multiple primary tumors (P<0.05), while sarcopenia was more prevalent in female patients with diabetes (P<0.05).

The results of stratified univariate logistic regression followed by multivariate logistic regression for different sex were shown in Table 5. For male subgroup, multivariable logistic regression confirmed BMI (OR =0.940; 95% CI: 0.892–0.991; P=0.021), number of primary tumors (OR =3.386; 95% CI: 1.815–6.316; P<0.001), ECOG status (OR =2.025; 95% CI: 1.436–2.856; P<0.001), and SUVmax of muscle (OR =0.155; 95% CI: 0.067–0.360; P<0.001) as independent predictors of sarcopenia. For female subgroup, multivariate logistic regression identified that diabetic status (OR =1.917; 95% CI: 1.073–3.426; P=0.028) and SUVmax of muscle (OR =0.065; 95% CI: 0.019–0.226; P<0.001) were independently predictive of sarcopenia.

Discussion

Sarcopenia has been shown to be associated with poor clinical outcomes in elderly patients with a variety of malignancies (17,18). This condition is closely associated with heightened vulnerability to the negative effects of cancer therapy, a reduced physical reserve, and sometimes, an incapability to persist with cancer treatment (19-21). Consequently, early assessment and intervention for sarcopenia could significantly benefit the prognosis of elderly cancer patients. ¹⁸F-FDG PET/CT is a routine examination for the diagnosis, staging, and assessment of treatment response in various malignancies, which can be used to not only measure SMI, but also obtain glucometabolic parameters of body composition such as SUVmax of muscle and fat. Skeletal muscle is a crucial organ for insulin-mediated glucose uptake, and sarcopenia may lead to changes in muscle glucose metabolism (22). Our previous studies revealed that the incidence of sarcopenia in cancer patients increases with a decline in muscle glucometabolism (11,12). Therefore, this study examines the association of muscle glucometabolic parameter extracted from ¹⁸F-FDG PET/CT and clinical pathological information with sarcopenia in elderly cancer patients.

This study demonstrated that maleness, low BMI, and reduced SUVmax of muscle were associated with an increased likelihood of sarcopenia. Compared to non-sarcopenic patients, those with sarcopenia had lower SUVmax of muscle, consistent with our previous studies and Xu et al. (11,12,23). In both rodents and humans with sarcopenia, skeletal muscle shows loss of rapidly contracting type II muscle fibers and atrophy, as well as fatty infiltration within and between muscle fibers (24). Escriva et al. (25) and Shulman (26) found that loss of glycolytic type II muscle fibers and insulin resistance due to fat infiltration into the muscle impaired glucose transport activity and glycogen synthesis in muscle, resulting in decreased glucose utilization in aged skeletal muscle. Haaparanta et al. (27) and Williams et al. (28) demonstrated that such changes in glucose metabolism can be mimicked in rats and human skeletal muscle using ¹⁸F-FDG. This evidence is consistent with our findings and suggests that a decline in SUVmax of muscle reflects a decrease in muscle glucose metabolism and is associated with an increased risk of sarcopenia. Vanitcharoenkul et al. (29) also showed that men had a higher prevalence of sarcopenia compared to women, and that the age-specific difference in prevalence between men and women widened significantly with age. Conversely, studies by Gao et al. (30) and Chien et al. (31) found that sarcopenia was more likely to occur in elderly female populations. This may be due to different diagnostic criteria for sarcopenia as well as differing sample populations. Besides, low BMI, reflecting the low level of nutritional reserves in an individual, was considered to be associated with the occurrence of sarcopenia in various types of cancer (32,33).

In addition, this study also found that poor ECOG status and multiple primary tumors were correlated with a higher incidence of sarcopenia. ECOG performance status is a practical tool used to assess the overall health condition and daily activity capacity of oncology patients and is often utilized to assist in clinical decision-making and prognosis (34). An adverse ECOG status indicates a reduction in the quality of life and self-care ability of patients, which is associated with higher levels of inactivity, potentially facilitating muscle loss and leading to the occurrence of sarcopenia. The incidence rate of multiple primary tumors in the present study was 9.8%, which was within the reported range of 1.2–12.2% (35-37). Currently, there is no direct research on the relationship between multiple primary tumors and sarcopenia. However, patients with multiple tumors generally face a higher risk of cachexia, and sarcopenia is considered a hallmark of cancer-associated cachexia (4). Therefore, we hypothesize that patients with multiple primary tumors may face higher physical consumption, which collectively increases the risk of sarcopenia. Moreover, in the general population, age is one of the most important factors of sarcopenia (38). However, possibly because our cohort consisted of patients aged 60 years and above, we did not observe the predictive value of age for sarcopenia. Given that measuring SUVmax of muscle is more feasible in clinical practice than calculating SMI, we also established a sarcopenia nomogram prediction model based on sex, BMI, ECOG status, number of primary tumors, and SUVmax of muscle, which is expected to be incorporated into routine clinical applications.

The criteria for diagnosing sarcopenia differ by sex; hence we also conducted a subgroup analysis based on sex stratification for factors related to sarcopenia. Regardless of sexes, the SUVmax of muscle was found to independently predict sarcopenia. This further confirms the feasibility of using the SUVmax of muscles as an alternative to the complex SMI calculations for diagnosing sarcopenia. Moreover, the difference between sex subgroups was that sarcopenia was more likely observed in elderly male patients with low BMI, poor ECOG status, and multiple primary tumors, while in elderly female patients with diabetes. The independent predictive factors for sarcopenia in the male population were consistent with the entire population, possibly because men accounted for 62.3% of our study cohort, making the analysis results for the entire population more influenced by males. Furthermore, malnutrition and poor overall physical condition may be key factors leading to sarcopenia in male patients. In contrast, the emergence of sarcopenia in female patients seemed to be more associated with metabolic issues, especially given the higher risk displayed by diabetic patients. This could be due to the muscle metabolic disorders caused by diabetic-induced abnormalities in glucose metabolism and insulin resistance (39). Consistent with our research, studies by Park et al. (40) and Chen et al. (41) also revealed that elderly female diabetic patients were at a higher risk of developing sarcopenia than males. These differences suggest that when addressing sarcopenia in elderly patients with malignancies, sex factors should be considered to lay the foundation for the development of more personalized and precise treatment strategies. For elderly male patients with malignancies, the strategy may need to focus more on improving nutritional status and enhancing physical strength, while for female patients, attention should also be paid to managing and regulating diabetes and its related metabolic issues. Such differentiated approaches can help more effectively slow down or prevent the development of sarcopenia, thereby improving the quality of life and treatment outcomes for elderly cancer patients.

This study has several limitations. First, it is a retrospective single-center study involving only an Asian cohort and lacked an independent external validation, which may reduce the generalizability of our findings to other populations. Second, the study cohort includes a variety of cancer patients, among which lung cancer patients account for 55.5%—more than half—which might introduce bias. Further studies are also needed to investigate the correlation between different tumor subtypes and sarcopenia. Third, other essential parameters, such as patients’ waist-hip circumference, arm-calf circumference, physical fitness tests, malnutrition status, vitamin D levels, and clinical frailty scale (CFS) scores, were not included in the current study and are still in need of further research. Fourth, sarcopenia due to different etiologies other than aging, such as medications and chronic inflammatory diseases, were not explored in the current study; however, this also warrant further research. Additionally, as patients did not undergo cold exposure during PET imaging, brown adipose tissue (BAT) was not activated and visualized, preventing us from investigating the correlation between BAT and sarcopenia in this cohort. Finally, the lack of patient follow-up prevented us from investigating prognostic factors. However, this study identified factors independently related to sarcopenia and established a model that uses only clinically feasible measurement methods to predict the likelihood of sarcopenia, as an alternative to the complex SMI measurement method, to assist in clinical management.

Conclusions

Regardless of sex, SUVmax of muscle was independently predictive of sarcopenia in elderly cancer patients with newly diagnosed malignancies. As an alternative to the complex SMI measurement method, a nomogram model including SUVmax of muscle could easily and effectively predict the likelihood of sarcopenia.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-705/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-705/dss

Funding: This work was supported by funds from the National Key R&D Program of China (No. 2024YFF0509200), the National Natural Science Foundation of China (No. 81971645), and the Guangdong Provincial People’s Hospital (No. KY0120211130).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-705/coif). All authors report the funding from the National Key R&D Program of China (No. 2024YFF0509200), the National Natural Science Foundation of China (No. 81971645), and the Guangdong Provincial People’s Hospital (No. KY0120211130). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics committee of Guangdong Provincial People’s Hospital (No. KY2024-534-01), and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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